The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.
DATA AUGMENTATION TECHNIQUES AND TRANSFER LEARNING APPROACHES APPLIED TO FACI...ijaia
ย
The face expression is the first thing we pay attention to when we want to understand a personโs state of
mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research
field. In this paper, because the small size of available training datasets, we propose a novel data
augmentation technique that improves the performances in the recognition task. We apply geometrical
transformations and build from scratch GAN models able to generate new synthetic images for each
emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with
different architectures. To measure the generalization ability of the models, we apply extra-database
protocol approach, namely we train models on the augmented versions of training dataset and test them on
two different databases. The combination of these techniques allows to reach average accuracy values of
the order of 85% for the InceptionResNetV2 model.
This document summarizes a research paper that empirically tests a model based on general deterrence theory regarding how user awareness of security countermeasures impacts perceptions of punishment certainty and severity for information systems misuse. The study developed hypotheses about the relationships between security policies, security education/training/awareness programs, computer monitoring, perceived certainty and severity of sanctions, and intentions for systems misuse. Data was collected through an online survey of professionals and analyzed using partial least squares and other statistical techniques. The results provided support for most of the hypotheses and showed that security countermeasures influence perceptions of punishment, which impact misuse intentions.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE...ijaia
ย
The principal objective of this work is to be able to use artificial intelligence techniques to be able to
design a predictive model of the performance of a third-generation mobile phone base radio, using the
analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of
these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used.
which will allow faster progress in the deep analysis of large amounts of data statistics and get better
results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base
radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case,
several models were generated based on in various artificial intelligence technique for the prediction of
performance results of a mobile phone base radio of third generation, the same ones that after several tests
were creation of a predictive model that determines the performance of a mobile phone base radio. As a
conclusion of this work, it was determined that the development of a predictive model based on artificial
intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict
complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a
radio base of third generation mobile telephony, these data were obtained through the operator's remote
monitoring and management tool Sure call PRS.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
ย
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
ย
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
Gender classification using custom convolutional neural networks architecture IJECEIAES
ย
Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed convolutional neural network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-ofthe-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.
IRJET- Face Recognition by Additive Block based Feature ExtractionIRJET Journal
ย
The document describes a proposed method for face recognition using additive block-based feature extraction. The method uses Chirp Z-Transform (CZT) and Goertzel algorithm for preprocessing to perform illumination normalization. It then divides the preprocessed image into blocks of equal size and superimposes them to extract features from the combined block. Gray Level Co-occurrence Matrix (GLCM) is used to further extract texture features. Euclidean distance classification is used to measure similarity between trained and test images. The proposed approach is tested on benchmark datasets and demonstrates better performance compared to existing methods in handling pose and illumination variations.
IRJET- Age Analysis using Face Recognition with Hybrid AlgorithmIRJET Journal
ย
1) The document presents a study on age analysis of human faces using a hybrid algorithm combining multiple methods including convolutional neural networks.
2) Facial images are collected then preprocessed and trained on a detection model to extract features. A convolutional neural network model is used to classify ages from the extracted features.
3) The results found the proposed hybrid algorithm approach achieved 96.7% accuracy in age prediction from faces, outperforming existing methods. This shows promise for accurate automatic age analysis.
DATA AUGMENTATION TECHNIQUES AND TRANSFER LEARNING APPROACHES APPLIED TO FACI...ijaia
ย
The face expression is the first thing we pay attention to when we want to understand a personโs state of
mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research
field. In this paper, because the small size of available training datasets, we propose a novel data
augmentation technique that improves the performances in the recognition task. We apply geometrical
transformations and build from scratch GAN models able to generate new synthetic images for each
emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with
different architectures. To measure the generalization ability of the models, we apply extra-database
protocol approach, namely we train models on the augmented versions of training dataset and test them on
two different databases. The combination of these techniques allows to reach average accuracy values of
the order of 85% for the InceptionResNetV2 model.
This document summarizes a research paper that empirically tests a model based on general deterrence theory regarding how user awareness of security countermeasures impacts perceptions of punishment certainty and severity for information systems misuse. The study developed hypotheses about the relationships between security policies, security education/training/awareness programs, computer monitoring, perceived certainty and severity of sanctions, and intentions for systems misuse. Data was collected through an online survey of professionals and analyzed using partial least squares and other statistical techniques. The results provided support for most of the hypotheses and showed that security countermeasures influence perceptions of punishment, which impact misuse intentions.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE MODELING OF A 3G MOBILE PHONE BASE...ijaia
ย
The principal objective of this work is to be able to use artificial intelligence techniques to be able to
design a predictive model of the performance of a third-generation mobile phone base radio, using the
analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of
these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used.
which will allow faster progress in the deep analysis of large amounts of data statistics and get better
results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base
radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case,
several models were generated based on in various artificial intelligence technique for the prediction of
performance results of a mobile phone base radio of third generation, the same ones that after several tests
were creation of a predictive model that determines the performance of a mobile phone base radio. As a
conclusion of this work, it was determined that the development of a predictive model based on artificial
intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict
complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a
radio base of third generation mobile telephony, these data were obtained through the operator's remote
monitoring and management tool Sure call PRS.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
ย
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
ย
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
Gender classification using custom convolutional neural networks architecture IJECEIAES
ย
Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed convolutional neural network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-ofthe-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.
IRJET- Face Recognition by Additive Block based Feature ExtractionIRJET Journal
ย
The document describes a proposed method for face recognition using additive block-based feature extraction. The method uses Chirp Z-Transform (CZT) and Goertzel algorithm for preprocessing to perform illumination normalization. It then divides the preprocessed image into blocks of equal size and superimposes them to extract features from the combined block. Gray Level Co-occurrence Matrix (GLCM) is used to further extract texture features. Euclidean distance classification is used to measure similarity between trained and test images. The proposed approach is tested on benchmark datasets and demonstrates better performance compared to existing methods in handling pose and illumination variations.
IRJET- Age Analysis using Face Recognition with Hybrid AlgorithmIRJET Journal
ย
1) The document presents a study on age analysis of human faces using a hybrid algorithm combining multiple methods including convolutional neural networks.
2) Facial images are collected then preprocessed and trained on a detection model to extract features. A convolutional neural network model is used to classify ages from the extracted features.
3) The results found the proposed hybrid algorithm approach achieved 96.7% accuracy in age prediction from faces, outperforming existing methods. This shows promise for accurate automatic age analysis.
This document summarizes Stefan Taubenberger's PhD research on using business process security requirements for IT security risk assessment. The research aims to determine if IT security risks can be reliably evaluated solely based on assessing adherence to security requirements, without using probabilities and events. The approach involves modeling business processes, identifying critical assets and security requirements, and evaluating how well security controls and processes meet the requirements. Preliminary validation using a reinsurance company's processes supports the idea that risks can be determined this way. The research seeks to address limitations of traditional risk assessment approaches.
Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectiv...YogeshIJTSRD
ย
The aim of information retrieval systems is to retrieve relevant information according to the query provided. The queries are often vague and uncertain. Thus, to improve the system, we propose an Automatic Query Expansion technique, to expand the query by adding new terms to the user s initial query so as to minimize query mismatch and thereby improving retrieval performance. Most of the existing techniques for expanding queries do not take into account the degree of semantic relationship among words. In this paper, the query is expanded by exploring terms which are semantically similar to the initial query terms as well as considering the degree of relationship, that is, โfuzzy membership-ย between them. The terms which seemed most relevant are used in expanded query and improve the information retrieval process. The experiments conducted on the queries set show that the proposed Automatic query expansion approach gave a higher precision, recall, and F measure then non fuzzy edge weights. Tarun Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia "Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45074.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/45074/automatic-query-expansion-using-word-embedding-based-on-fuzzy-graph-connectivity-measures/tarun-goyal
Development of durian leaf disease detection on Android device IJECEIAES
ย
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponentโs objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
Face Annotation using Co-Relation based Matching for Improving Image Mining ...IRJET Journal
ย
This document discusses face annotation techniques for improving image mining in videos. It begins by introducing the need for better image retrieval with the rise of online sharing. It then discusses challenges with face annotation in videos and existing techniques like content-based image retrieval and search-based face annotation. The document analyzes limitations of these existing techniques, such as semantic gaps with manual tagging, decreased accuracy, and poor generalization with new data. It proposes using correlation-based matching to address problems in face recognition techniques.
This document summarizes several research papers on human face recognition using feature extraction and measurements. It discusses using face recognition for applications like surveillance, access control, and banking validation. Key steps in face recognition systems include extracting features from captured images, comparing them to known images in a training database, and identifying errors like false acceptance and false rejection rates. Methods discussed for feature extraction and dimensionality reduction include Linear Discriminant Analysis and Principal Component Analysis. The document also examines factors that affect face recognition performance like illumination changes, aging, and expressions. Quantifying uncertainty in face recognition algorithms is identified as important for evaluating system performance.
IRJET- A Review on Fake Biometry DetectionIRJET Journal
ย
This document summarizes a review on detecting fake biometrics. It discusses how face recognition technology has advanced but is vulnerable to spoof attacks using fake faces. The paper presents a novel software-based method for detecting fraudulent access attempts across multiple biometric systems like iris and face recognition. Experimental results on public datasets show the proposed method performs competitively compared to other state-of-the-art approaches. It analyzes the general image quality of real biometric samples to distinguish them from fake traits efficiently.
IRJET- Breast Cancer Prediction using Deep LearningIRJET Journal
ย
This document discusses using deep learning to predict breast cancer based on tumor data. It proposes using a neural network model to classify tumors as malignant or benign. The key steps are:
1. Collecting and preprocessing tumor cell data to remove noise and inconsistencies.
2. Developing a neural network model and training it on labeled training data to learn patterns.
3. Testing the trained model on unlabeled testing data to evaluate its accuracy in classifying tumors.
The goal is to develop an accurate model to help doctors determine the critical condition of patients and classify difficult tumors.
LAN Based HF Radio Simulator An Approach to Develop an Early Prototype for Lo...YogeshIJTSRD
ย
This document summarizes the key aspects of developing a LAN-based HF radio simulator. It conducted surveys and interviews with users to understand requirements. The surveys found that a simulator could facilitate training by allowing practice without actual radios. A web-based platform was preferred. The document outlines the methodology, which includes specifying user and software requirements. It defines functional and non-functional requirements for the simulator. The simulator aims to allow trainees to practice operating radios in different scenarios while preserving performance records.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
ย
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
ZERNIKE-ENTROPY IMAGE SIMILARITY MEASURE BASED ON JOINT HISTOGRAM FOR FACE RE...AM Publications
ย
The direction of image similarity for face recognition required a combination of powerful tools and stable in case of any challenges such as different illumination, various environment and complex poses etc. In this paper, we combined very robust measures in image similarity and face recognition which is Zernike moment and information theory in one proposed measure namely Zernike-Entropy Image Similarity Measure (Z-EISM). Z-EISM based on incorporates the concepts of Picard entropy and a modified one dimension version of the two dimensions joint histogram of the two images under test. Four datasets have been used to test, compare, and prove that the proposed Z-EISM has better performance than the existing measures
Keerthi Thomas is a PhD student at the Open University, supervised by Prof. Bashar Nuseibeh, Dr. Arosha Bandara, and Mr. Blaine Price. Her research focuses on eliciting and analyzing users' privacy requirements for mobile applications. Previous work has shown privacy requirements vary based on users' changing contexts. However, most existing approaches do not address challenges of understanding privacy needs for mobile apps. Thomas proposes a systematic approach using a user-centric model combining contextual and interaction data to capture how privacy requirements are "distilled" from empirical studies of mobile social networking app users.
This document summarizes research on intrusion detection systems using data mining techniques. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using models, a data warehouse to store data and models, and a model generator to develop and distribute new models. It then discusses supervised and unsupervised learning approaches for intrusion detection. The document concludes by summarizing several papers on intrusion detection using techniques like neural networks, decision trees, clustering, and ensemble methods.
IRJET- Comparative Study of Machine Learning Models for Alzheimerโs Detec...IRJET Journal
ย
This document presents a comparative study of machine learning models for detecting Alzheimer's disease. The study uses MRI data to extract features like brain volume and uses those as inputs to various machine learning models like logistic regression, support vector machines, decision trees, random forests and AdaBoost. The performance of each model is evaluated using metrics like accuracy, sensitivity and specificity. The results show that the random forest model performs the best with the highest prediction rates, indicating it has potential for accurate early detection of Alzheimer's using MRI data.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
ย
This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
ย
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASAโs turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
deep learning applications in medical image analysis brain tumorVenkat Projects
ย
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
ย
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Preprocessing Techniques for Image Mining on Biopsy ImagesIJERA Editor
ย
This document discusses preprocessing techniques for image mining on biopsy images. It begins with an introduction to biomedical imaging and image mining. The key steps in image mining are described as image retrieval, preprocessing, feature extraction, data mining, and interpretation. Various preprocessing techniques are then evaluated on biopsy images, including interpolation, thresholding, and segmentation. Bicubic interpolation and Otsu thresholding produced good results for enhancing renal biopsy images. Overall, the document evaluates different preprocessing methods and their effects on biopsy images to help extract meaningful features for disease detection through image mining.
Novel framework for optimized digital forensic for mitigating complex image ...IJECEIAES
ย
Digital Image Forensic is significantly becoming popular owing to the increasing usage of the images as a media of information propagation. However, owing to the presence of various image editing tools and software, there is also an increasing threat to image content security. Reviewing the existing approaches to identify the traces or artifacts states that there is a large scope of optimization to be implemented to enhance the processing further. Therefore, this paper presents a novel framework that performs cost-effective optimization of digital forensic technique with an idea of accurately localizing the area of tampering as well as offers a capability to mitigate the attacks of various forms. The study outcome shows that the proposed system offers better outcomes in contrast to the existing system to a significant scale to prove that minor novelty in design attributes could induce better improvement with respect to accuracy as well as resilience toward all potential image threats.
1) The document discusses copy-move forgery detection using the Discrete Wavelet Transform (DWT) method. Copy-move forgery involves copying and pasting a part of an image within the same image to conceal information.
2) Previous work has used the PCA algorithm to detect incompatible pixels, but this study proposes using the DWT and GLCM algorithms instead. The proposed algorithm is tested in MATLAB and evaluated based on PSNR and MSE values.
3) The study finds that the proposed DWT and GLCM algorithm performs better than the previous PCA-only approach, providing more accurate forgery detection while maintaining good performance metrics.
Vehicle Driver Age Estimation using Neural NetworksIRJET Journal
ย
This document presents research on developing a convolutional neural network model to estimate the age and gender of a vehicle driver from their facial image. The researchers assembled a large dataset of over 60,000 face images from various sources to train their CNN model. They implemented the model using Caffe and tested it on a Raspberry Pi 3B+ for real-time age and gender detection. After training, the CNN model was able to accurately classify age and gender from input images with an accuracy of 98.1%. The document discusses the CNN architecture, preprocessing steps, and algorithms used to develop this age and gender detection system for vehicle drivers.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
ย
The document summarizes research on facial age estimation using transfer learning and Bayesian optimization based on gender information. Specifically:
1) A convolutional neural network is trained to classify gender from facial images. This gender classification CNN is then used as input for an age estimation model.
2) Bayesian optimization is applied to the pre-trained gender classification CNN to fine-tune it for the age estimation task. This reduces error on validation data.
3) Experiments on the FERET and FG-NET datasets show the proposed approach of using gender information and Bayesian optimization outperforms state-of-the-art methods, achieving a mean absolute error of 1.2 and 2.67 respectively.
This document summarizes Stefan Taubenberger's PhD research on using business process security requirements for IT security risk assessment. The research aims to determine if IT security risks can be reliably evaluated solely based on assessing adherence to security requirements, without using probabilities and events. The approach involves modeling business processes, identifying critical assets and security requirements, and evaluating how well security controls and processes meet the requirements. Preliminary validation using a reinsurance company's processes supports the idea that risks can be determined this way. The research seeks to address limitations of traditional risk assessment approaches.
Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectiv...YogeshIJTSRD
ย
The aim of information retrieval systems is to retrieve relevant information according to the query provided. The queries are often vague and uncertain. Thus, to improve the system, we propose an Automatic Query Expansion technique, to expand the query by adding new terms to the user s initial query so as to minimize query mismatch and thereby improving retrieval performance. Most of the existing techniques for expanding queries do not take into account the degree of semantic relationship among words. In this paper, the query is expanded by exploring terms which are semantically similar to the initial query terms as well as considering the degree of relationship, that is, โfuzzy membership-ย between them. The terms which seemed most relevant are used in expanded query and improve the information retrieval process. The experiments conducted on the queries set show that the proposed Automatic query expansion approach gave a higher precision, recall, and F measure then non fuzzy edge weights. Tarun Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia "Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45074.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/45074/automatic-query-expansion-using-word-embedding-based-on-fuzzy-graph-connectivity-measures/tarun-goyal
Development of durian leaf disease detection on Android device IJECEIAES
ย
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponentโs objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
Face Annotation using Co-Relation based Matching for Improving Image Mining ...IRJET Journal
ย
This document discusses face annotation techniques for improving image mining in videos. It begins by introducing the need for better image retrieval with the rise of online sharing. It then discusses challenges with face annotation in videos and existing techniques like content-based image retrieval and search-based face annotation. The document analyzes limitations of these existing techniques, such as semantic gaps with manual tagging, decreased accuracy, and poor generalization with new data. It proposes using correlation-based matching to address problems in face recognition techniques.
This document summarizes several research papers on human face recognition using feature extraction and measurements. It discusses using face recognition for applications like surveillance, access control, and banking validation. Key steps in face recognition systems include extracting features from captured images, comparing them to known images in a training database, and identifying errors like false acceptance and false rejection rates. Methods discussed for feature extraction and dimensionality reduction include Linear Discriminant Analysis and Principal Component Analysis. The document also examines factors that affect face recognition performance like illumination changes, aging, and expressions. Quantifying uncertainty in face recognition algorithms is identified as important for evaluating system performance.
IRJET- A Review on Fake Biometry DetectionIRJET Journal
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This document summarizes a review on detecting fake biometrics. It discusses how face recognition technology has advanced but is vulnerable to spoof attacks using fake faces. The paper presents a novel software-based method for detecting fraudulent access attempts across multiple biometric systems like iris and face recognition. Experimental results on public datasets show the proposed method performs competitively compared to other state-of-the-art approaches. It analyzes the general image quality of real biometric samples to distinguish them from fake traits efficiently.
IRJET- Breast Cancer Prediction using Deep LearningIRJET Journal
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This document discusses using deep learning to predict breast cancer based on tumor data. It proposes using a neural network model to classify tumors as malignant or benign. The key steps are:
1. Collecting and preprocessing tumor cell data to remove noise and inconsistencies.
2. Developing a neural network model and training it on labeled training data to learn patterns.
3. Testing the trained model on unlabeled testing data to evaluate its accuracy in classifying tumors.
The goal is to develop an accurate model to help doctors determine the critical condition of patients and classify difficult tumors.
LAN Based HF Radio Simulator An Approach to Develop an Early Prototype for Lo...YogeshIJTSRD
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This document summarizes the key aspects of developing a LAN-based HF radio simulator. It conducted surveys and interviews with users to understand requirements. The surveys found that a simulator could facilitate training by allowing practice without actual radios. A web-based platform was preferred. The document outlines the methodology, which includes specifying user and software requirements. It defines functional and non-functional requirements for the simulator. The simulator aims to allow trainees to practice operating radios in different scenarios while preserving performance records.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
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This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
ZERNIKE-ENTROPY IMAGE SIMILARITY MEASURE BASED ON JOINT HISTOGRAM FOR FACE RE...AM Publications
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The direction of image similarity for face recognition required a combination of powerful tools and stable in case of any challenges such as different illumination, various environment and complex poses etc. In this paper, we combined very robust measures in image similarity and face recognition which is Zernike moment and information theory in one proposed measure namely Zernike-Entropy Image Similarity Measure (Z-EISM). Z-EISM based on incorporates the concepts of Picard entropy and a modified one dimension version of the two dimensions joint histogram of the two images under test. Four datasets have been used to test, compare, and prove that the proposed Z-EISM has better performance than the existing measures
Keerthi Thomas is a PhD student at the Open University, supervised by Prof. Bashar Nuseibeh, Dr. Arosha Bandara, and Mr. Blaine Price. Her research focuses on eliciting and analyzing users' privacy requirements for mobile applications. Previous work has shown privacy requirements vary based on users' changing contexts. However, most existing approaches do not address challenges of understanding privacy needs for mobile apps. Thomas proposes a systematic approach using a user-centric model combining contextual and interaction data to capture how privacy requirements are "distilled" from empirical studies of mobile social networking app users.
This document summarizes research on intrusion detection systems using data mining techniques. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using models, a data warehouse to store data and models, and a model generator to develop and distribute new models. It then discusses supervised and unsupervised learning approaches for intrusion detection. The document concludes by summarizing several papers on intrusion detection using techniques like neural networks, decision trees, clustering, and ensemble methods.
IRJET- Comparative Study of Machine Learning Models for Alzheimerโs Detec...IRJET Journal
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This document presents a comparative study of machine learning models for detecting Alzheimer's disease. The study uses MRI data to extract features like brain volume and uses those as inputs to various machine learning models like logistic regression, support vector machines, decision trees, random forests and AdaBoost. The performance of each model is evaluated using metrics like accuracy, sensitivity and specificity. The results show that the random forest model performs the best with the highest prediction rates, indicating it has potential for accurate early detection of Alzheimer's using MRI data.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
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This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
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In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASAโs turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
deep learning applications in medical image analysis brain tumorVenkat Projects
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The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
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Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Preprocessing Techniques for Image Mining on Biopsy ImagesIJERA Editor
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This document discusses preprocessing techniques for image mining on biopsy images. It begins with an introduction to biomedical imaging and image mining. The key steps in image mining are described as image retrieval, preprocessing, feature extraction, data mining, and interpretation. Various preprocessing techniques are then evaluated on biopsy images, including interpolation, thresholding, and segmentation. Bicubic interpolation and Otsu thresholding produced good results for enhancing renal biopsy images. Overall, the document evaluates different preprocessing methods and their effects on biopsy images to help extract meaningful features for disease detection through image mining.
Novel framework for optimized digital forensic for mitigating complex image ...IJECEIAES
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Digital Image Forensic is significantly becoming popular owing to the increasing usage of the images as a media of information propagation. However, owing to the presence of various image editing tools and software, there is also an increasing threat to image content security. Reviewing the existing approaches to identify the traces or artifacts states that there is a large scope of optimization to be implemented to enhance the processing further. Therefore, this paper presents a novel framework that performs cost-effective optimization of digital forensic technique with an idea of accurately localizing the area of tampering as well as offers a capability to mitigate the attacks of various forms. The study outcome shows that the proposed system offers better outcomes in contrast to the existing system to a significant scale to prove that minor novelty in design attributes could induce better improvement with respect to accuracy as well as resilience toward all potential image threats.
1) The document discusses copy-move forgery detection using the Discrete Wavelet Transform (DWT) method. Copy-move forgery involves copying and pasting a part of an image within the same image to conceal information.
2) Previous work has used the PCA algorithm to detect incompatible pixels, but this study proposes using the DWT and GLCM algorithms instead. The proposed algorithm is tested in MATLAB and evaluated based on PSNR and MSE values.
3) The study finds that the proposed DWT and GLCM algorithm performs better than the previous PCA-only approach, providing more accurate forgery detection while maintaining good performance metrics.
Vehicle Driver Age Estimation using Neural NetworksIRJET Journal
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This document presents research on developing a convolutional neural network model to estimate the age and gender of a vehicle driver from their facial image. The researchers assembled a large dataset of over 60,000 face images from various sources to train their CNN model. They implemented the model using Caffe and tested it on a Raspberry Pi 3B+ for real-time age and gender detection. After training, the CNN model was able to accurately classify age and gender from input images with an accuracy of 98.1%. The document discusses the CNN architecture, preprocessing steps, and algorithms used to develop this age and gender detection system for vehicle drivers.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
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The document summarizes research on facial age estimation using transfer learning and Bayesian optimization based on gender information. Specifically:
1) A convolutional neural network is trained to classify gender from facial images. This gender classification CNN is then used as input for an age estimation model.
2) Bayesian optimization is applied to the pre-trained gender classification CNN to fine-tune it for the age estimation task. This reduces error on validation data.
3) Experiments on the FERET and FG-NET datasets show the proposed approach of using gender information and Bayesian optimization outperforms state-of-the-art methods, achieving a mean absolute error of 1.2 and 2.67 respectively.
Age and Gender Prediction and Human countIRJET Journal
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This document presents a system for age and gender prediction and human counting using deep convolutional neural networks. The system is trained on the Adience benchmark dataset containing over 26,000 images labeled with age and gender. A caffe model is used to perform the predictions, which are then deployed on a website. The model achieves 88% accuracy for age and gender prediction. The system also counts the number of faces in an image using a simple CNN architecture. The purpose is to demonstrate how machine learning can be used to detect age, gender, and count humans in images with reduced manual effort.
Age and Gender Classification using Convolutional Neural NetworkIRJET Journal
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This document describes a study that aims to accurately identify the gender and age range of facial images using convolutional neural networks. It begins with an introduction to age and gender classification and some of the challenges. It then discusses the system analysis and design, including the use of convolutional neural networks and machine learning techniques. The implementation section notes that Python, TensorFlow, Keras and OpenCV will be used to build a convolutional neural network model to detect faces in images and predict age and gender through training on available datasets. The overall goal is to develop an accurate system for age and gender detection from facial images.
IRJET- Facial Expression Recognition using Deep Learning: A ReviewIRJET Journal
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This document provides a review of facial expression recognition using deep learning approaches. It begins with an introduction to facial expression recognition and its applications. It then discusses commonly used datasets for facial expression recognition, including image-based datasets like JAFFE and video-based datasets like CK+. The document reviews 26 previous research papers that used deep learning methods like convolutional neural networks for facial expression recognition. It concludes that convolutional neural networks provide more accurate results for facial expression recognition compared to traditional methods.
Progression in Large Age-Gap Face VerificationIRJET Journal
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1. The document discusses progression in large age-gap face verification. It summarizes various techniques used for face recognition from conventional methods to deep neural networks.
2. A typical face recognition system includes image acquisition, face detection, normalization, feature extraction, and then matching. The document reviews several feature extraction methods like Eigenfaces, Fisherfaces, and local binary patterns.
3. Recent deep learning approaches like convolutional neural networks have improved face recognition accuracy. One study used CNNs with metric learning and achieved high accuracy on the Labeled Faces in the Wild dataset. Encoding image microstructures and identity factor analysis has also been effective for matching similar faces.
FACE VERIFICATION ACROSS AGE PROGRESSION USING ENHANCED CONVOLUTION NEURAL NE...sipij
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This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.
Face Verification Across Age Progression using Enhanced Convolution Neural Ne...sipij
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This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a
texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand
to develop robust methods to verify facial images when they age. In this paper, a deep learning method
based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG)
and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and
classification
Person identification based on facial biometrics in different lighting condit...IJECEIAES
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Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individualโs identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
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Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26589.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/26589/face-recognition-for-human-identification-using-brisk-feature-and-normal-distribution-model/khin-mar-thi
IRJET- Persons Identification Tool for Visually Impaired - Digital EyeIRJET Journal
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This document presents a face detection and recognition system to help visually impaired people identify individuals. The system uses computer vision techniques like convolutional neural networks and cascade classifiers for face detection with high accuracy. It then performs face recognition on pre-trained image datasets to determine a person's identity, as well as their emotion, age and gender. The system was tested on a combined dataset of images and achieved 95.7% accuracy in identifying faces, even when there were many faces present. This person identification tool aims to help the visually impaired better interact with others by audibly providing the name and attributes of detected individuals.
Ensemble-based face expression recognition approach for image sentiment anal...IJECEIAES
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The document presents an ensemble-based facial expression recognition (FER) model for image sentiment analysis. It combines three classification models - a customized convolutional neural network (CNN), ResNet50, and InceptionV3. The predictions from the three models are averaged using an ensemble classifier method to determine the final classification. The model is trained and tested on the FER-2013 dataset containing uncontrolled images. Experimental results show the ensemble model outperforms individual models in classifying some expressions like happy and neutral, achieving accuracies of 91.7% and 81.7% respectively. However, for other expressions like disgust and anger, ResNet50 performs better. The ensemble model achieves an overall accuracy of 72.3% for FER.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
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Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
This document summarizes a research paper on image-based static facial expression recognition using multiple deep convolutional neural networks. The researchers used an ensemble of face detectors to locate faces in images, then classified the facial expressions using an ensemble of CNN models pre-trained on a larger dataset and fine-tuned on the SFEW 2.0 dataset. They proposed two methods for learning the ensemble weights of the CNN models by minimizing log likelihood or hinge loss. Their method achieved state-of-the-art results on the FER dataset and 61.29% accuracy on the SFEW 2.0 test set, significantly above the baseline.
IRJET- A Review on Various Approaches of Face RecognitionIRJET Journal
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This document reviews various approaches for face recognition. It begins by describing challenges in face recognition related to scale, pose, illumination, and disguise. It then discusses principal component analysis (PCA) and local discriminant analysis (LDA), which are appearance-based approaches, as well as local binary pattern (LBP) and local ternary pattern (LTP), which are texture-based approaches. PCA uses eigenfaces to represent facial features while LDA aims to preserve discriminating information between classes. LBP and LTP extract texture features from facial images for recognition. The document concludes LDA generally provides better accuracy than PCA for whole-face recognition, while LTP performs better than other methods for texture-based recognition as it is more
This project report describes research on using convolutional neural networks to classify gender and age from facial images. The goal is to automatically estimate a person's gender and age based solely on their facial appearance in an image. The report provides background on related work, describes the dataset collected from LinkedIn profiles, and explains the methodology used, including logistic regression and CNN models. The CNN approach achieved 81% accuracy for gender classification and 68% for age classification on test data. Areas for future improvement are also discussed, such as collecting more training data across all age groups.
IMBALANCED DATASET EFFECT ON CNN-BASED CLASSIFIER PERFORMANCE FOR FACE RECOGN...gerogepatton
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Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems.
Imbalanced Dataset Effect on CNN-Based Classifier Performance for Face Recogn...gerogepatton
ย
Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems.
A Survey on different techniques used for age and gender classificationIRJET Journal
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This document summarizes research on techniques for age and gender classification from facial images. It reviews 6 papers that used various methods including convolutional neural networks (CNN), conditional probability neural networks, and GoogleNet. CNN achieved the highest accuracy of 96% for age and gender classification. Conditional probability neural networks achieved 72% accuracy for age classification and 94% for gender. The document also compares datasets like Kaggle, IMDB-WIKI, and Adience that were used and finds CNN to be the most effective technique for this task.
This document proposes a methodology for recognizing facial expressions with increased speed and accuracy. It involves detecting faces, preprocessing images through greyscaling and contrast adjustment, extracting features through local binary patterns, and classifying expressions. The proposed system includes face detection using Haar classifiers, image processing steps, feature detection by analyzing distances between action points on facial characteristics, and using a classifier to output the expression. Future work involves testing this approach against other methods using specialized datasets and hardware to develop a faster and more reliable facial expression recognition system.
Similar to Comparative analysis of augmented datasets performances of age invariant face recognition models (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
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The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 ร 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
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The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
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In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
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The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptographyโs concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Plant leaf identification system using convolutional neural networkjournalBEEI
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This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
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This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Understanding the role of individual learner in adaptive and personalized e-l...journalBEEI
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Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
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Comparative analysis of augmented datasets performances of age invariant face recognition models
1. Bulletin of Electrical Engineering and Informatics
Vol. 10, No. 3, June 2021, pp. 1356~1367
ISSN: 2302-9285, DOI: 10.11591/eei.v10i3.3020 ๏ฒ 1356
Journal homepage: http://beei.org
Comparative analysis of augmented datasets performances of
age invariant face recognition models
Kennedy Okokpujie1
, Etinosa Noma-Osaghae2
, Samuel Ndueso John3
, Charles Ndujiuba4
,
Imhade Princess Okokpujie5
1,2
Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria
3
Department of Electrical and Electronic Engineering, Nigerian Defence Academy, Kaduna, Nigeria
4
Department of Electrical and Electronic Engineering, Air Force Institute of Technology, Kaduna, Nigeria
5
Department of Mechanical Engineering, Covenant University, Ota, Nigeria
Article Info ABSTRACT
Article history:
Received Aug 23, 2020
Revised Oct 12, 2020
Accepted Apr 14, 2021
The popularity of face recognition systems has increased due to their non-
invasive method of image acquisition, thus boasting the widespread
applications. Face ageing is one major factor that influences the performance
of face recognition algorithms. In this study, the authors present a
comparative study of the two most accepted and experimented face ageing
datasets (FG-Net and morph II). These datasets were used to simulate age
invariant face recognition (AIFR) models. Four types of noises were added to
the two face ageing datasets at the preprocessing stage. The addition of noise
at the preprocessing stage served as a data augmentation technique that
increased the number of sample images available for deep convolutional
neural network (DCNN) experimentation, improved the proposed AIFR
model and the trait aging features extraction process. The proposed AIFR
models are developed with the pre-trained Inception-ResNet-v2 deep
convolutional neural network architecture. On testing and comparing the
models, the results revealed that FG-Net is more efficient over Morph with
an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of
39% and mean absolute error (MAE) of -0.63%.
Keywords:
Age invariant face recognition
Convolutional neural network
Data augmentation
Fg-Net dataset
Morph dataset
This is an open access article under the CC BY-SA license.
Corresponding Author:
Kennedy Okokpujie
Department of Electrical and Information Engineering
Covenant University
Km 10 Idiroko Road, Ota, Ogun State, Nigeria
Email: kennedy.okokpujie@covenantuniversity.edu.ng
1. INTRODUCTION
The paper aim at carrying out a comparative analysis of augmented datasets (FG-Net dataset and
morph datasets). Both performances (accuracy, loss function, mean square error (MSE), and mean absolute
error (MAE)) for trait-ageing invariant face recognition (AIFR) systems are compared. The significate of the
study is that both datasets are used for AIFR. Data augmentation via the addition of noises to both datasets at
the preprocessing phase greatly increases the accuracy and other parameters of AIFR.
- Literature review
Many comparisons exist in the literature between the performances of augmented datasets on age
invariant recognition systems. The augmented dataset is usually used independently of each other to verify
the invariability of designed face recognition systems. Two of the most common face image datasets used in
age-invariant face recognition, FG-NET and MORPH [1] are usually at the centre of comparisons made to
check the performance of age-invariant face recognition system. The goal is to have a good performance for
2. Bulletin of Electr Eng & Inf ISSN: 2302-9285 ๏ฒ
Comparative analysis of augmented datasets performances of age invariant faceโฆ (Kennedy Okokpujie)
1357
all datasets used for face recognition. The results got from augmented standard datasets [2] for face images
are usually based on the robustness of designed face recognition system models to variations in pose,
illumination, shape, and texture. It is customary to have several ways of augmenting the datasets based on the
goal of the researcher and the challenge that needs to be solved.
Comparisons between the performance of augmented datasets on age-invariant face recognition
systems extend to niche applications like finding missing children who are discovered at a much later time
(longer than ten years) [3]. The importance of comparisons, especially for niche applications, is emphasized
in [4]. The factors that degrade the performance of face recognition systems are so numerous that it is
sensible to have as many augmented datasets from as many providers as possible. The abundant evidence of
the robustness of any age-invariant face recognition system is usually presented after it has passed the
rigorous condition of being subject to varieties of augmented datasets [5]. The evidence is generally in the
form of performance metrics like accuracy [6]. These performance metrics are used to gauge how well face
recognition systems can accurately recognize face images of various subjects regardless of the source of the
image, the noise added to the image and other forms of augmentation.
The region of the face used [7] to develop the age-invariant face recognition model plays a
significant role in the design of age-invariant face recognition systems that are robust. The region of the face,
when extracted from various datasets, could give non-identical performances on the designed face
recognition model. This submission extends to other face recognition models designed to checkmate the
negative effect of trait ageing. At the centre of comparisons of augmented datasets is accuracy [8], [9]. The
precision with which the designed face recognition model can identify subjects' facial image after they have
been designed to discriminate between real and generated images, estimate age and identify subjects. New
applications of age-invariant face recognition systems like soft biometrics [10] take the comparison between
augmented datasets seriously. The verification/identification process is thoroughly confirmed for as many
augmented datasets as possible to verify the accuracy of the face recognition system. The algorithms used to
develop age-invariant face recognition systems such as support vector machine (SVM) [11], principal
component analysis (PCA) and the like, perform differently for various forms of augmented datasets. The
authors in [12] tested the recognition system performance of a modelled age-invariant face recognition
system after passing face images through the designed and optimized adaptive neuro-fuzzy inference system
(ANFIS) classifier. The reviews made in [13] and [14] give in-depth studies of the performances of various
augmented datasets on designed age-invariant face recognition system. The studies focused on the challenges
of face recognition as it relates to the verification of designed face recognition systems using different
augmented datasets. The studies were able to identify the challenges faced by adaptive and age-invariant face
recognition systems through extensive and thorough comparisons using different augmented datasets.
- Fg-Net dataset specifications and complexities
There are 1002 images of 82 various persons with ages spanning from birth to 69 years in the FG-
NET database. The most common age group in the dataset is within the (<41 years) age group. Some of the
pictures of subjects in the FG-NET database were digitally taken recently while others were scanned copies
of the original photographs taken from personal collections of the subjects. The quality of the images in the
FG-NET database depends significantly on the skill of the photographer, the condition of the photograph, the
sophistication of the imaging tools used and the durability of the photographic paper found in personal
collections. Thus there are variations in sharpness, illumination, resolution, background, facial expression,
camera angles, and facial hair. These variations make the FG-NET database a good one for AIFR research
and samples of same subject (person) ranging from ages 2 to 43 is as shown in Figure 1.
Figure 1. Image age progression of Fg-Net dataset subject 1 at ages 1,5,8,10,14,16,โฆโฆ28,29,33,40,43
- Morph dataset II specifications and complexities
A longitudinal face database, MORPH Album 2 is a well-known publicly available dataset for face
recognition research. The face images in the MORPH database vary in age, sex, background. The MORPH
3. ๏ฒ ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 3, June 2021 : 1356 โ 1367
1358
dataset was collected in uncontrolled environments (the pictures were taken in real-world conditions) and
thus has a very unique range of facial expressions. The photographs in the MORPH database were taken over
a period of four years and the database is regularly updated. MORPH Album 2 contains 55,134 face images
of 13,000 subjects along with metadata that shows that majority of the images were acquired in a period of
four years. Example images, age progression, and statistics of MORPH Album 2, as shown in Figure 2.
Figure 2(a) for white male and Figure 2(b) for african-american female.
(a) (b)
Figure 2. These figures are, (a) Image progression for white male, (b) Image progression for african-american
female
- Comparative analysis of Fg-Net ageing dataset and morph dataset II
Some of the remarkable dissimilarity between the Fg-Net and the Morph datasets is that children
dominate photos from Fg-Net. In contrast, most pictures from Morph are mainly from adult persons [15].
Also, the age gap between the images of the same subjects in the Fg-Net dataset is significantly wide-ranging
as compare to the once in Morph dataset, which is relatively small [16], as shown in Table 1. Besides, Fg-Net
contains subjects from one caucasian race, whereas Morph dataset contains the caucasoid, negroid, and
mongoloid races [17]. Furthermore, the total images (samples) in Fg-Net are 1002 with 82 subjects, while
that of Morph is 55,134 with 13,658 subjects [18]-[22], while details of both datasets are as shown in Table 2
and Table 3 and Table 4 depicts the Morph numbers of facial image and decade-of-life. However, the
Similarity is that both datasets contain face images of the same subjects at various age gaps. The sole reason
makes both ageing datasets and can be compared experimentally base on this fact [5]-[8].
Table 1. Comparison of FGNET and MORPH ageing datasets [23]
Database Images Subjects Age range Resolution
FG-NET ageing
database
1,002 high-resolution colour
or grey-scale
82 multiple race
subjects
0 to 69 years High 294 images of
females 430 images of males
High
MORPH database Album 1 1,724 face images 515 46 days to 29 years 240ร200 pixels
Album 2 more than 20,000 4,000 16 to 77 years -
Table 2. Number of sample images in each age group of FG-NET dataset [24]
Age group (in years) Number of samples
0-9 371
10-19 339
20-29 144
30-39 79
40-49 46
50-59 15
60-69 8
Total 1,002
Table 3. Number of facial images based on gender and ethnicity from MORPH dataset [25]
African European Asian Hispanic Other Total
Male 36,832 7,961 141 1,667 44 46,645
Female 5,757 2,598 13 102 19 8,489
Total 42,589 10,559 154 1,769 63 55,134
4. Bulletin of Electr Eng & Inf ISSN: 2302-9285 ๏ฒ
Comparative analysis of augmented datasets performances of age invariant faceโฆ (Kennedy Okokpujie)
1359
Table 4. Morph numbers of facial image and decade-of-life [26]
Age group (in years) Number of samples โMale Number of samples โFemale Total Number of Samples
< 20 6,638 831 7,469
20-29 14,016 2,309 16,325
30-39 12,447 2,910 15,357
40-49 10,062 1,988 12,050
50+ 3,482 451 3,933
Total 46,645 8,489 55,134
2. RESEARCH METHOD
2.1. Pre-processing the FG-NET database for deep learning
A mammoth amount of data is needed to train a deep neural network. The FG-NET dataset has only
10-15 face images of each subject at different ages amounting to 1002 images. The size of the FG-NET
dataset is too small for deep neural network application. We preprocessed the images in the database by
adding noise to it. The addition of noise to the FG-NET dataset helped increase the total amount of pictures
available for deep learning application. The augmentation of the dataset was done at the preprocessing stage
to allow for improved feature extraction. The following steps were followed to augment the FG-NET dataset
with noise.
a. Convert all images to three channels with matrix entries for red, green an blue (RGB) for uniformity.
b. Viola-Jones face detector crops all face images and removes all background details from the face images
for richer feature extraction by the proposed deep learning model.
c. Five different versions of each image is created by the addition of four types of noise namely:
โ No noise (original cropped image preserved)
โ Poisson noise
โ Salt and pepper noise
โ Speckle noise
โ Gaussian noise
The number of images available for deep learning experimentation was increased from 1002 to 5010
with up to about 45-90 images per subject. The addition of noise also helped with getting the deep neural
network to extract richer features from the face image for AFIR. Algorithm 1 shows the noise injection image
(data augmentation) procedures.
2.2. Pre-processing the morph database for deep learning
A mammoth amount of data is needed to train a deep neural network. The MORPH Album 2 dataset
has only 1-5 face images of each subject at different ages amounting to 13,000 images. The size of the is too
small for deep neural network application. We preprocessed the images in the database by adding noise to it.
The addition of noise to the MORPH Album 2 dataset helped increase the total amount of pictures available
for deep learning application. The augmentation of the dataset was done at the preprocessing stage to allow
for improved feature extraction. The following steps were followed to augment the MORPH Album 2 dataset
with noise:
a. Convert all images to three channels with matrix entries for red, green an blue (RGB) for uniformity.
b. Viola Jones face detector crops all face images and removes all background details from the face images
for richer feature extraction by the proposed deep learning model.
c. Five different versions of each image is created by the addition of four types of noise namely:
โ No noise (original cropped image preserved)
โ Poisson noise
โ Salt and pepper noise
โ Speckle noise
โ Gaussian noise
The number of images available for deep learning experimentation was increased from 13,000 to
27,5000 with up to about 5-25 images per subject. The addition of noise also helped with getting the deep
neural network to extract richer features from the face image for AFIR.
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2.3. Feature extraction and classification using convolutional neural network
Over a million images from the ImageNet database was used to train the Inception-ResNet-v2
convolutional neural network (CNN). The images that was used to train the Inception-ResNet-v2 CNN forms
part of the databased for the imagenet large-scale visual recognition challenge. Inception-ResNet-v2 has 164
layers and can classify images into 1000 object classes. The CNN accept images of size 299x299 for
classification. The Inception-ResNet-v2 was used in this study to learn features for age invariant face
recognition using a process called transfer learning. Transfer learning is the process of adapting a pre-trained
neural network for another task for which it was not originally trained. Transfer learning was used to learn
age invariant features from the FG-NET and MORPH datasets for AIFR. Figure 3 and Table 5 shows a
summary of the network architecture of Inception-ResNet-v2. In order to use the Inception-ResNet-v2
network, MATLAB R2018b was installed and downloaded the installer of the deep learning toolbox model
for Inception-ResNet-v2 network from [27]. Run the installer to install the Inception-ResNet-v2 network in
MATLAB R2018b.
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Figure 3. Block diagram of the adapted Inception-Resnet-V2 architecture network [28]
Table 5. Architecture of adapted Inception-Resnet-V2 network [29]
Block Type Repeat Depth Filter/Stride Output size Branch 1 Branch 2 Branch 3
1 Convolution 3ร3/2 149ร149ร32 (32)
1 Convolution 3ร3/1 147ร147ร32 (32)
1 Convolution 3ร3/1 147ร147ร64 (64)
1 Max Pooling 3ร3/2 73ร73ร160
1 Convolution 3ร3/2 73ร73ร160 (96)
1 Convolution 3x3/1 71ร71ร192 (64, 96) (64, 64,64,96)
1 Convolution 3ร3/2 35ร35ร384 (192)
1 Max Pooling 3x3/2 35ร35ร384
2 Inception-A 5 3 35x35x256 (32)
(32, 32/2)
(32,48,64/2)
3 Reduction-A 1 3 17x17x256 (384) (256,256,384)
4 Inception-B 10 3 17X17X896 (192) (128,160,192)
5 Reduction-B 1 3 8x8x1792
(256,384/2)
(256,288/2)
(256,288,320/2)
6 Inception-C 5 3 8x8x1792 (192) (192,224,256)
7
Average
Pooling
8ร8 1792
8 Dropout Keep 0.8 1792
9 Softmax Classifier 82/13000
2.4. Training the deep learning model
The preprocessed FG-NET dataset was used to retrain the Inception-ResNet-v2 neural network for
AIFR. The process was possible via Transfer learning. The transfer learning process is enumerated below:
a. The preprocessed FG-NET images are loaded into MATLAB using the image datastore object.
b. The images are then splited into a validation set (20% images) and a train set (80%).
c. The images in the train set are resized to 299x299 for compatipility with Inception-ResNetโv2.
d. Inception-ResNet_v2 is run by MATLAB.
e. Training preferences are specified.
f. Begin the transfer learning process using the augmented FG-NET dataset.
g. Check the validity of the transfer learning process using the validation set.
h. Estimate the networkโs accuracy.
2.5. Using the trained deep learning model for face recognition in FG-NET and Morph datasets
The retrained neural network was used for testing images from the Morph Album 2 dataset using the
following process:
a. Image is read from the MORPH Album 2 dataset.
b. All images are converted into an RGB matrix
c. Viola-Jones algorithm is used to cop and detect faces.
d. All images are resized to 299x299, resize the image to 299x299.
e. The retrained Inception-ResNet-v2 neural network is loaded.
f. Load the image into the retrained neural network for classification.
g. Compare predicted class to the ground truth.
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3. RESULTS AND DISCUSSION
3.1. Evaluation methodology
MAE, MSE, Accuracy, and Loss function were used to check the performance of the proposed
AIFR model [30]-[35].
3.1.1. Accuracy
Accuracy is derived from the true positive (TP), true negative (TN), false positive (FP), and false
negative (FN) values as shown in (1). True positives are correct positive classifications. True negatives are
correct negative classifications. False positives are wrong positive classifications and false negatives are
wrong negative classifications.
Accuracy =
TP+TN
TP+TN+FP+TN
โ 100% (1)
3.1.2. Mean squared error
The mean squared error (MSE) is a predictor value that is always positive. A score closer to zero
better. Where, N, in this instance, is the sums of iteration,f_i is the training loss values and y_i is the testing
loss values. Consequently, MSE is calculated, as presented in (2) [36], [37].
๐๐๐ธ =
1
๐
โ (๐๐ โ ๐ฆ๐)2
๐
๐=1 (2)
The MSE is the average (
1
๐
โ .
๐
๐=1 ) of the squares of the inaccuracies (๐๐ โ ๐ฆ๐)2
.
3.1.3. Mean absolute error
The mean absolute error (MAE) is a measure of the disparity between two values. In this
circumstance between y_i which is the values of training loss and y ฬ_i, which is the value of the testing loss,
n is the sums of iteration. Consequently, MAE is calculated, as presented in (3) [38].
MAE =
โ |๐ฆ๐โ๐ฆ
ฬ๐|
๐
๐=1
๐
(3)
The MAE is the mean of the total errors (|๐ฆ๐ โ ๐ฆ
ฬ๐|).
3.1.4. Loss function
Categorical cross-entropy is a loss function used to calculate the variation concerning two
probability disseminations. This dissimilarity is computed for respectively iteration in the training and testing
dataset. The technique to calculate the likelihood variation is as shown in the (4) [39].
โ๐๐๐๐ ๐ (๐ฆ,ลท) = โ โ .
๐
๐ก=1 โ ๐ฆ๐
๐ก
. log(ลท๐
๐ก
)
๐ถ
๐=1 (4)
Where ๐ฅ is the input value, ๐ฆ is the true value, ลท is forecast value by the method, ๐ is the sum of iteration and
๐ถ is the sum of class labels. Wen et al. [40] recommended a loss function called centre loss in adding to using
the definite cross-entropy loss. The idea is to growth the discriminative power of the completely learned
features by declining the intra-class variations. The centre loss function is
as shown in (5).
โ๐๐๐๐ก๐๐(๐ฆ,ลท) =
1
2
โ .
๐
๐ก=1 โ (ลท๐
๐ก
โ ๐๐ฆ๐
๐ก
)2
๐ถ
๐=1 (5)
While ๐๐ฆ๐
is the ๐ฆ๐๐กโ class centre of the features and ๐ is the sum of iterations. Wen et al. [40] detected that
(5) seen not accomplish the expected result. Two modifications were done by Wen et al. [40] to decide this
problem. First, the modification is to bring up to data the centers founded on a mini-batch as a additional for
the entire dataset. For the second modification is the institution of two new variables ๐ผ and the ๐ฟ โ
๐๐ข๐๐๐ก๐๐๐. ฮฑ is used to regulate the learning rates of the centre, and the ฮด-function is a Boolean that results in
1 if the situation is true and 0 if the situation is false. In (6) defines the updated function of the class centre.
ฮ๐๐ (๐ฆ, ลท) =
โ ๐ฟ(๐ฆ๐
๐
๐ก=1 =๐).(๐๐โลท๐)
1+โ ๐ฟ(๐ฆ๐=๐)
๐
๐ก=1
(6)
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The novel centre of each class is as shown in (7):
๐๐
๐ก+1
= ๐๐
๐ก
โ ๐ผ. ฮ๐๐
๐ก
(7)
While ๐ผ โ [0, 1]. Wen et al., [40] introduce ฮป to balance the two-loss functions of the total loss function.
The complete function is shown in (8).
โ = โ๐๐๐๐ ๐ + ๐โ๐๐๐๐ก๐๐ (8)
In the event ๐ is set to 0, the total loss function is equal to the categorical cross-entropy function is used.
3.2. Results and discussion
This section deal wth the results and comparative analysis of augmented datasets (FG-Net and
Morph II) performances for trait-ageing invariant face fecognition system. Figure 4 shows FG-Net and
Morph datasets training, and testing accuracies results in comparative analysis. With FG-Net dataset
outperforming the Morph dataset with a mean testing accuracy of 0.15%. While Figure 5 shows FG-Net and
Morph training and testing loss (error) results in comparative analysis. With mean FG-Net dataset output
performance, the Morph dataset testing loss of 71%. Table 6 shows a summary of the result performance of
augmented datasets of Fg-Net and Morph dataset. All this implied that FG-Net dataset have will perform
better than Morph dataset during deployment of these model in age invariant face recognition (AIFR) system
Table 6. The performance of augmented datasets (Fg-Net and Morph II)
Variable Fg-Net Dataset Morph Dataset Percentage Difference
Accuracy (Testing) 99.94% 99.79% 0.15%
Loss Function (Testing) 0.0039% 0.0067% 71%
Mean Square Error (MSE) 0.0155 0.0094 39%
Mean Absolute Error (MAE) 0.0634 0.0638 -0.63%
Furthermore, Figure 4 emphasize in graphical form the characteristics of FG-Net and morph training
and testing accuracies results in comparative analysis. While Figure 5 highlights in graphical form the
attributes of FG-Net and Morph training and testing loss (error function) results in comparative analysis.
Figure 6 in graphical form the characteristics of FG-Net and morph squared error results comparative
analysis. Finally, Figure 7 in graphical form the attributes of FG-Net and morph absolute error results
comparative analysis.
Figure 4. FG-Net and morph training and testing accuracies results comparative analysis (the result is best
viewed in colour)
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Figure 5. FG-Net and morph training and testing loss (error function) results in comparative analysis
(the result is best viewed in colour)
Figure 6. FG-Net and morph squared error results comparative analysis (the result is best viewed in colour)
Figure 7. FG-Net and Morph absolute error results comparative analysis (the result is best viewed in colour)
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4. CONCLUSION
This paper compared two of the most acceptable and experimented face ageing dataset (FG-NET
and Morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. The
obtained results show that FG-Net and Morph datasets are similar, and the little difference may be due to
randomness for augmenting the dataset.
ACKNOWLEDGMENTS
This paper is sponsor by Covenant University Center of Research, Innovation, and Discovery
(CUCRID) Covenant University, Ota, Ogun State, Nigeria.
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BIOGRAPHIES OF AUTHORS
Kennedy Okokpujie holds a Bachelor of Engineering (B.Eng.) in Electrical and Electronics
Engineering, Master of Science (M.Sc.) in Electrical and Electronics Engineering, Master of
Business Administration (MBA), Master of Engineering (M.Eng.) in Electronics and
Telecommunication Engineering and Ph.D in Information and Communication Engineering. Dr.
Kennedy Okokpujie is currently a Researcher/Lecturer with the Department of Electrical and
Information Engineering at Covenant University, Ota, Ogun State, Nigeria. He is a member
numerous organizations including; The Nigeria Society of Engineers and the Institute of
Electrical and Electronic Engineers (IEEE) and reviews for numerous high impact Journals. His
research areas of interest include Biometrics, Artificial Intelligence, Communication
Engineering, Digital signal Processing, Cryptography and its applications, Network security and
Pravicy.
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Etinosa Noma-Osaghae is with the Department of Electrical and Information Engineering. He
has a masterโs degree in Electronic and Telecommunication Engineering. Information Theory,
Signal Processing, Biometrics, E-learning, E-health and Internet of Things are his research areas
of interest.
Samuel Ndueso John is a Professor of Computer Systems and Network Engineering. He has his
higher education at Donetsk National Technical University, Ukraine where he successfully
defended and obtained B.Sc, M.SC, MPhil and Ph.D. degrees in Computer Systems and Network
Engineering, specializing in Computer Science, Computing Machines, Complex Systems,
Security and Networks in 1993, 1994, 2000 and 2005, respectively. Presently, John is a
Professor of Computer Systems and Network Engineering in the Department of
Electrical/Electronic Engineering, Faculty of Engineering and Technology, Nigerian Defence
Academy, Kaduna, Nigeria. John has acquired valuable knowledge and practical experience in
the use of information technology as an enabler of industrial and national development goals. He
has a vast knowledge of computing and has applied it in the pursuance of a wide range of
indigenous ICT Convergence, Data Efficiency Management, Cyber Security, Cybercrime and
Telemedicine solutions.
Charles U. Ndujiuba is a professor of Communication Engineering with Air Force Institute of
Technology Nigerian Air Force, Kaduna state, Nigeria. Prof. C. U. Ndujiuba holds a Ph.D. in
Electrical & Electronics Engineering from the University College London (University of
London); Master Specialize (Masters with Specialization) in Radio Communications from Ecole
Superieure dโElectricite (SUPELEC) France; MSc in Electrical Engineering from the University
of Lagos; BSc in Electronics & Communications Engineering from the London Metropolitan
University. Prof. Ndujiuba is a Chartered Electronics Engineer (CEng) and a highly skilled
wireless professional. He has more than 25 years of RF, Microwave, Fixed-line (SDH and PDH),
and PMR experience, with considerable international exposure. Prof. Ndujiuba has attended
several conferences and published many technical papers in major professional journals. Prior to
joining Covenant University Ota Nigeria in 2011, Ndujiuba was the Technical Director of Globe
Trunk Ltd UK. His research interests include Monolithic Microwave Integrated Circuits
(MMIC), Active Filters, Ultra-Low Noise Amplifiers, Active Devices and Circuits, UWB
Transmitter, Modelling & Simulation, Dielectric Resonator Antennas, and Detection and
Collision Avoidance of Unmanned Aerial Vehicles.
Engr. Dr. (Mrs) Okokpujie Imhade Princess is a researcher/lecturer in the Department of
Mechanical Engineering Covenant University, Ota, Ogun State Nigeria. She is currently the
Chief Editor to Covenant Journal of Engineering Technology (CJET). She is also a reviewer and
editor of many international/local journals and conferences. Her areas of research interest are
Design and Production, Advanced Manufacturing such as Machining, Tool Wear, Vibration,
Nano-lubricant, Energy Systems, Mathematical Modeling, Optimization, Mechatronics and also
a Multi-disciplinary Researcher. Dr. I. P. Okokpujie is an active researcher who has authored/co-
authored over 106 peer-reviewed publications in reputable journals and international
conferences. In 2017 to 2019, she was the technical secretary to the International Conference on
Engineering for a Sustainable World (ICESW) index in Scopus and ISI data based through the
IOPs publisher. She is a Registered Engineer of the Council for the Regulation of Engineering in
Nigeria (COREN), a member of the Nigerian Society of Engineers (NSE), member Nigerian
Institution of Mechanical Engineers and Association of Professional Women Engineers of
Nigeria (APWEN). She is currently the National Technical Secretary and Vice-Chairman of
APWEN Ota Chapter in Ogun State, also, the Technical Editor to Journal of Nigeria Institution
of Mechanical Engineers. She is one of the top-rated researchers in her institution, and she is
happily married and blessed with children. Dr. I. P. Okokpujie is very passionate about the
Education of the Girl Child and also offers quality mentorship to the Young Engineers.