The document summarizes a modified CNN-based face recognition system that achieves improved accuracy rates over traditional CNN models. Preprocessing techniques like histogram equalization, self-quotient image, locally tuned inverse sine nonlinear, gamma intensity correction, and difference of Gaussian are applied to CNN models to further improve accuracy. On the Extended Yale B database, the proposed CNN model achieves an accuracy of 96.2% without preprocessing, and 99.8% with preprocessing. On the FERET database, accuracy improves from 71.4% without preprocessing to 76.3% with preprocessing.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MRI Image Segmentation by Using DWT for Detection of Brain Tumorijtsrd
Brain tumor segmentation is one of the critical tasks in the medical image processing. Some early diagnosis of brain tumor helps in improving the treatment and also increases the survival rate of the patients. The manual segmentation for cancer diagnosis of brain tumor and generation of MRI images in clinical routine is difficult and time consuming. The aim of this research paper is to review of MRI based brain tumor segmentation methods for the treatment of cancer like diseases. The magnetic resonance imaging used for detection of tumor and diagnosis of tissue abnormalities. The computerized medical image segmentation helps the doctors in treatment in a simple way with fast decision making. The brain tumor segmentation assessed by computer based surgery, tumor growth, developing tumor growth models and treatment responses. This research focuses on the causes of brain tumor, brain tumor segmentation and its classification, MRI scanning process and different segmentation methodologies. Ishu Rana | Gargi Kalia | Preeti Sondhi ""MRI Image Segmentation by Using DWT for Detection of Brain Tumor"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25116.pdf
Paper URL: https://www.ijtsrd.com/computer-science/bioinformatics/25116/mri-image-segmentation-by-using-dwt-for-detection-of-brain-tumor/ishu-rana
Brain Tumor Detection Using Image ProcessingSinbad Konick
The process of brain tumor detection using various filters and finding out the best possible approach. Processing the image and using other filters and find out the result.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
A chi-square-SVM based pedagogical rule extraction method for microarray data...IJAAS Team
Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
Breast cancer is one of the most common diseases diagnosed among female cancer patients. Early detection of breast cancer is needed to reduce the risk of fatality of this disease as no cure has been found yet for this illness. This research is conducted to improve the Gradient Vector Flow (GVF) Snake Active Contour segmentation technique in mammography segmentation. Segmentation of the mammogram image is done to segment lesions existence using Chan-Vese Active Contour and Localized Active Contour. Besides that, the effectiveness of these both methods are then compared and chosen to be the best method. Digital Database of Screening Mammograms (DDSM) is used for the purpose of screening. First, the images undergo pre-processing process using the Gaussian Low Pass Filter to remove unwanted noise. After that, contrast enhancement applied to the images. Segmentation of mammograms is then conducted by using Chan-Vese Active Contour and Localized Active Contour method. The result shows that Chan-Vese technique outperforms Localized Active Contour with 90% accuracy.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MRI Image Segmentation by Using DWT for Detection of Brain Tumorijtsrd
Brain tumor segmentation is one of the critical tasks in the medical image processing. Some early diagnosis of brain tumor helps in improving the treatment and also increases the survival rate of the patients. The manual segmentation for cancer diagnosis of brain tumor and generation of MRI images in clinical routine is difficult and time consuming. The aim of this research paper is to review of MRI based brain tumor segmentation methods for the treatment of cancer like diseases. The magnetic resonance imaging used for detection of tumor and diagnosis of tissue abnormalities. The computerized medical image segmentation helps the doctors in treatment in a simple way with fast decision making. The brain tumor segmentation assessed by computer based surgery, tumor growth, developing tumor growth models and treatment responses. This research focuses on the causes of brain tumor, brain tumor segmentation and its classification, MRI scanning process and different segmentation methodologies. Ishu Rana | Gargi Kalia | Preeti Sondhi ""MRI Image Segmentation by Using DWT for Detection of Brain Tumor"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25116.pdf
Paper URL: https://www.ijtsrd.com/computer-science/bioinformatics/25116/mri-image-segmentation-by-using-dwt-for-detection-of-brain-tumor/ishu-rana
Brain Tumor Detection Using Image ProcessingSinbad Konick
The process of brain tumor detection using various filters and finding out the best possible approach. Processing the image and using other filters and find out the result.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
A chi-square-SVM based pedagogical rule extraction method for microarray data...IJAAS Team
Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
Breast cancer is one of the most common diseases diagnosed among female cancer patients. Early detection of breast cancer is needed to reduce the risk of fatality of this disease as no cure has been found yet for this illness. This research is conducted to improve the Gradient Vector Flow (GVF) Snake Active Contour segmentation technique in mammography segmentation. Segmentation of the mammogram image is done to segment lesions existence using Chan-Vese Active Contour and Localized Active Contour. Besides that, the effectiveness of these both methods are then compared and chosen to be the best method. Digital Database of Screening Mammograms (DDSM) is used for the purpose of screening. First, the images undergo pre-processing process using the Gaussian Low Pass Filter to remove unwanted noise. After that, contrast enhancement applied to the images. Segmentation of mammograms is then conducted by using Chan-Vese Active Contour and Localized Active Contour method. The result shows that Chan-Vese technique outperforms Localized Active Contour with 90% accuracy.
IMBALANCED DATASET EFFECT ON CNN-BASED CLASSIFIER PERFORMANCE FOR FACE RECOGN...gerogepatton
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
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 NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
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 NASA’s 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
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
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 NASA’s 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
Paper Explained: RandAugment: Practical automated data augmentation with a re...Devansh16
RandAugment: Practical automated data augmentation with a reduced search space is a paper that proposes a new Data Augmentation technique that outperforms all current techniques while being cheaper.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is
appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access
control, and electronic customer relationship management. Current deep learning-based methods have
displayed encouraging performance in age estimation field. Males and Females have a variable type of
appearance aging pattern; this results in age differently. This fact leads to assuming that using gender
information may improve the age estimator performance. We have proposed a novel model based on
Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then
Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task.
Bayesian Optimization reduces the classification error on the validation set for the pre-trained model.
Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The
experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian
Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute
Error (MAE) of 1.2 and 2.67 respectively.
A hybrid approach for face recognition using a convolutional neural network c...IAESIJAI
Facial recognition technology has been used in many fields such as security,
biometric identification, robotics, video surveillance, health, and commerce
due to its ease of implementation and minimal data processing time.
However, this technology is influenced by the presence of variations such as
pose, lighting, or occlusion. In this paper, we propose a new approach to
improve the accuracy rate of face recognition in the presence of variation or
occlusion, by combining feature extraction with a histogram of oriented
gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the
Canny contour detector techniques, as well as a convolutional neural
network (CNN) architecture, tested with several combinations of the
activation function used (Softmax and Segmoïd) and the optimization
algorithm used during training (adam, Adamax, RMSprop, and stochastic
gradient descent (SGD)). For this, a preprocessing was performed on two
databases of our database of faces (ORL) and Sheffield faces used, then we
perform a feature extraction operation with the mentioned techniques and
then pass them to our used CNN architecture. The results of our simulations
show a high performance of the SIFT+CNN combination, in the case of the
presence of variations with an accuracy rate up to 100%.
End-to-end deep auto-encoder for segmenting a moving object with limited tra...IJECEIAES
Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.
A DEEP LEARNING APPROACH FOR SEMANTIC SEGMENTATION IN BRAIN TUMOR IMAGESPNandaSai
Digital image processing is vast fields which can be using various applications. Which include Detection of criminal face, fingerprint authentication system, in medical field, object recognition etc. Brain tumor detection plays an important role in medical field. Brain tumor detection is detection of tumor affected part in the brain along with its shape size and boundary, so it useful in medical field.
Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologist and are important for planning of treatment strategies, monitoring of disease progression and prediction of patient outcome. For a better understanding of the pathophysiology of diseases, quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures
Dataset Pre-Processing and Artificial Augmentation, Network Architecture and ...INFOGAIN PUBLICATION
Training a Convolutional Neural Network (CNN) based classifier is dependent on a large number of factors. These factors involve tasks such as aggregation of apt dataset, arriving at a suitable CNN network, processing of the dataset, and selecting the training parameters to arrive at the desired classification results. This review includes pre-processing techniques and dataset augmentation techniques used in various CNN based classification researches. In many classification problems, it is usually observed that the quality of dataset is responsible for proper training of CNN network, and this quality is judged on the basis of variations in data for every class. It is not usual to find such a pre-made dataset due to many natural concerns. Also it is recommended to have a large dataset, which is again not usually made available directly as a dataset. In some cases, the noise present in the dataset may not prove useful for training, while in others, researchers prefer to add noise to certain images to make the network less vulnerable to unwanted variations. Hence, researchers use artificial digital imaging techniques to derive variations in the dataset and clear or add noise. Thus, the presented paper accumulates state-of-the-art works that used the pre-processing and artificial augmentation of dataset before training. The next part to data augmentation is training, which includes proper selection of several parameters and a suitable CNN architecture. This paper also includes such network characteristics, dataset characteristics and training methodologies used in biomedical imaging, vision modules of autonomous driverless cars, and a few general vision based applications.
A new approachto image classification based on adeep multiclass AdaBoosting e...IJECEIAES
In recent years, deep learning methods have been developed in order to solve the problems. These methods were effective in solving complex problems. Convolution is one of the learning methods. This method is applied in classifying and processing of images as well. Hybrid methods are another multi-component machine learning method. These methods are categorized into independent and dependent types. Ada-Boosting algorithm is one of these methods. Today, the classification of images has many applications. So far, several algorithms have been presented for binary and multi-class classification. Most of the above-mentioned methods have a high dependence on the data. The present study intends to use a combination of deep learning methods and associated hybrid methods to classify the images. It is presumed that this method is able to reduce the error rate in images classification. The proposed algorithm consists of the Ada-Boosting hybrid method and bi-layer convolutional learning method. The proposed method was analyzed after it was implemented on a multi-class Mnist data set and displayed the result of the error rate reduction. The results of this study indicate that the error rate of the proposed method is less than Ada-Boosting and convolution methods. Also, the network has more stability compared to the other methods.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
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Generating a custom Ruby SDK for your web service or Rails API using Smithy
A Modified CNN-Based Face Recognition System
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
DOI: 10.5121/ijaia.2021.12201 1
A MODIFIED CNN-BASED FACE
RECOGNITION SYSTEM
Jayanthi Raghavan and Majid Ahmadi
Department of Electrical and Computer Engineering,
University of Windsor, Windsor, Canada
ABSTRACT
In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract
unique facial features and softmax classifier is applied to classify facial images in a fully connected layer
of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes
and low value of learning rate, showed that the proposed model has improved the face recognition
accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B
database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and
DoG are applied to the CNN model. After the application of preprocessing techniques, the improved
accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET
Database with frontal face, before the application of preprocessing techniques, CNN model yields the
maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy
is improved to 76.3%
KEYWORDS
CNN, ANN, GPU
1. INTRODUCTION
For human brain, recognizing face is a very simple task and can be performed fast. In computer
vision, face recognition is a very challenging task. Even though the face recognition research is in
an advanced state [7], till now it is not possible to obtain results on par with humans.
To date, many approaches have been suggested for facial recognition. Holistic method works by
projecting facial images onto a low-dimensional space, which neglects surplus details and
variations that are not needed for the facial recognition [11]. One of the methods under this
category is PCA [2]. The holistic methods are sensitive to local distortions like facial expression
or illumination variation.
Subsequently, progress in the field of computer vision led to the growth of feature-based method
in which features are extracted from various parts of a face image. Feature-based methods are
robust to local variations such as intensity variation and face expression changes [22]. With the
development of the local feature descriptors, feature based methods gained popularity. Local
Binary Pattern (LBP) [6] is an extensively applied local feature descriptor in face recognition.
The recent trend is towards neural network-based approach [30]. Deep learning-based methods
achieve excellent results in many fields like robotics and autonomous driving cars [3]. Deep
learning methods are based on convolutional neural networks (CNNs). CNNs are slightlydifferent
from normal neural network. In CNN, neurons in convolutional layer are thinly connected to the
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
2
neurons in the next layer based on their relative location. CNNs are multilayer network trained
from end to end with raw image pixel values assigned to classifier outputs.
The main advantage of deep learning methods is that they can be trained with very big datasets to
learn the vital features to represent the input data. The main issue with deep learning method is
models trained with small datasets are having the problem of poor generalization, which results in
over-fitting.
Generalization term indicates the performance difference of a network model when assessed on
earlier viewed training data against the testing data, the network has never viewed before [8].
Models with poor generalizability have overfitted the training data.
Overfitting is a term used when the network model functions extremely good with the training
data, but could not work well with the test data. In the overfitted network validation error goes up
while the training error comes down [21].
To reduce the overfitting, regularization process is employed on the network model.
Regularization is a method of making minute changes to the actual network model and the
learning algorithm, so that the model functions better in both training and testing data set.
Regularization is defined as “Allowing to generalize well to unseen data even when training on a
finite training set or with an imperfect optimization procedure” [4]. There are various
regularization techniques available in machine learning like Dropout, Data Augmentation, Early
stopping, batch normalization etc. [10]. Some of the regularization techniques are explained
below.
Dropout means removing units temporarily in a neural network, together with all the incoming
and outgoing links [23]. Dropout can be explained as the regularization technique by including
noise to the hidden units of the network.
Another popular technique is batch normalization. Batch Normalization operates by deducting
the batch mean from each activation and dividing by the standard deviation of the batch [8]. The
normalization technique together with standardization is used as a typical combination in the
preprocessing of pixel values. Batch normalization technique can be employed to any individual
layer within the network. Hence it is powerful [5].
Data augmentation is an extensively applied technique in deep learning. The performance of the
deep neural network is based on the size of the dataset [45]. Deep learning network needs large
dataset to avoid the problem of overfitting. Sometimes it is difficult to get quality and huge
database especially in medical field. Data augmentation helps to artificially inflate the size of the
training dataset by the methods called data warping or oversampling. The augmented data is a
representation of detailed set of feasible data points, thus minimizing the distance between the
training and the validation set.
Data augmentation techniques are useful to provide powerful regularization in terms of improved
generalization which in turn yields better network performance [46]. Bengio et al [48]
demonstrate that the data augmentation techniques are very effective in deep networks compared
to shallow networks. In addition to flipping and cropping various techniques like color casting,
vignetting, rotation, horizontal and vertical stretching proven to be effective data augmentation
methods [49].
The main aim of augmentation technique is to diminish the effect of overfitting on models using
traditional transformations to manipulate the training data. As an example, a labeled image in the
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
3
dataset can be increased by various processes like flipping, rotation, morphing and zooming.
After some time, trained network gets exposure to such modifications and the it can identify the
same object with different variations.
There are some limitations associated with the application of data augmentation techniques. One
of the main disadvantages is it demands extra memory, computational expenses involved with
implementing augmentation methods and longer training time. Some geometric transformations
such as translation or random cropping must be monitored carefully to ensure that the labels of
the images are not altered during the transformation process [8].
Optimizers update the weight parameters to minimize the cost function. Cost function is defined
as the difference between predicted and actual output. One of the popular optimizers is Adam
optimizer [1].
Adam is an adaptive learning rate optimization algorithm, which calculates learning rates for
different parameters individually. Adam uses computations of first and second moments of
gradient to adjust the learning rate for each weight of the network.
Application of regularization methods help to improve the accuracy rates. To increase the
accuracy rates further, preprocessing techniques are applied to deep CNN architecture.
2. PREPROCESSING METHODS
Face recognition task becomes challenging due to illumination conditions, occlusion, pose and
facial expression variations [33]. The variation in illumination is one of the main challenging
problems which affects the performance of the face recognition system. Among all, shadowing
effect, underexposure, and overexposure conditions are challenging problems that need to be
addressed in the face recognition process [35]. If the lighting conditions present in the gallery
image is different from the probe image, then the process of face recognition may completely fail
[34]. A good face recognition system should be able to give accurate recognition rate under the
different illumination conditions between images of the same face [32]. Image enhancement
algorithms play a great role in handling the illumination variation.
The main aim of preprocessing is to remove features that obstruct the process of classifying the
images of the same person (within-class differences), thereby boosting the difference of them
with others (between-class differences) [36].
For better face recognition under uncontrolled and illumination variation conditions, the vital
features responsible for differentiating two different faces require to be retained. The shadows
produced in facial images due to variation in lighting directions may cause loss of important
facial features which are helpful for recognition. A preprocessing method must enhance the
intensity in the regions of inadequately illuminated and decrease the intensity in the densely
illuminated regions while retaining the intensity in the fairly illuminated portions [37]. Few
important preprocessing techniques are discussed below.
2.1. Histogram Equalization (HE)
HE [38] flattens the histogram of the image and expands the dynamic range of the pixel intensity
values by employing cumulative density function. The Histogram Equalization is a global
preprocessing technique.
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
4
For image I(x, y) with discrete k. gray values histogram is defined by the probability of
occurrence of the gray level I [39] is given by equation (1) as follows.
p(i) = 𝑛𝑖 /N (1)
Where i∈ 0, 1…k -1 grey level and N is total number of pixels in the image.
2.2. Self Quotient Image (SQI)
SQI [40] is one of the illumination invariant algorithm suggested for handling both shadow and
lighting changes. It is defined as the ratio of the intensity of the input image to its smooth version
as given in equations (2) and (3).
Q(x,y)=I(x,y)/S(x,y) (2)
= I(x,y) /(F(x,y) *I(x,y)) (3)
where I (x, y) is the face image and S (x, y) is a smoothed version of the image and ∗ is the
convolution operation. F is the smoothing kernel which in this case is a weighted Gaussian filter
and Q is the Self Quotient Image since it is derived from one image and has the same quotient
form as that in the quotient image method.
2.3. Locally Tuned Inverse Sine Nonlinear (LTISN)
LTISN [41] is a nonlinear and pixel by pixel approach, where the improved intensity values are
calculated by applying the inverse sine function with a tunable parameter based on the nearby
pixel values given in the equations (4), (5), (6), (7) and (8). The intensity range of the image is
rescaled to [0 1] followed by a nonlinear transfer function.
2.4.Gamma Intensity Correction (GIC)
GIC [42] is a nonlinear gray-level transformation that substitutes gray-level I with the gray level
𝐼
1
ϒ, given by the equation (9).
𝐼 = 𝐼
1
ϒ(9)
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
5
As shown in the (Fig.1), for the values of gamma less than 1.0 darkens the image and for the
values of gamma greater than 1.0 lightens the image. When gamma value is 1.0, it does not
produce any effect.
Fig.1. Gamma Intensity Correction
2.5. Difference of Gaussian (DoG)
DoG is a grayscale image enhancement algorithm [42] that involves the subtraction of one
blurred version of an original grayscale image from another less blurred version of the original.
The blurred images are obtained by convolving the original grayscale image with Gaussian
kernels having differing standard deviations [34], which is given in the equation (10).
𝜎1, 𝜎2 are Gaussian kernel widths.
2.6. Contrast-Limited Adaptive Histogram Equalization (CLAHE)
CLAHE works on small areas in the image, known as tiles, rather than the whole image [43].
Individual tile's contrast is improved. Hence histogram of the output area is roughly matching
with the histogram specified by the distribution parameter. The tiles present in the neighborhood
regions are then joined by applying bilinear interpolation to minimize the effect of artificially
induced border line [39]. In CLAHE, the image is divided into a limited number of regions and
the same histogram equalization technique is applied to pixels in each region [44].
3. CONVOLUTIONAL NEURAL NETWORK
Deep learning-based methods have shown better performances in terms of accuracy and speed of
processing in image recognition
3.1. CNN Basics
CNN is biologically inspired by visual cortex in brain [20]. Different phases of learning
procedure in CNN is similar to the visual cortex. The visual cortex has small group of cells that
are reactive to particular regions of the visual field. Hubel and Wiesel [17] experimentally
showed that particular group of neurons in the cat’s brain reacted to the appearance of edges of a
certain orientation as shown in the (Fig.2). Further they illustrated that one specific set of neurons
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
6
were activated, when exhibited to vertical edges and also another set of neurons fired when
exposed to horizontal or diagonal edges. All of these categories of neurons are arranged in a
columnar configuration and collectively these neurons can produce visual perception.
Different portions of the visual cortex are classified as V1, V2, V3, and V4. In general, V1 and
V2 regions of visual cortex are having close resemblance with convolutional and subsampling
layers, whereas inferior temporal region resembles the higher layers of CNN [16]. During
theprocess of training, CNN learns with the help of backpropagation algorithm by making
adjustments in weights with respect to the target.
Fig.2. Hubel and Wiesel experiment
3.2. CNN Architecture
In CNN architecture, network layers are divided into three types: the convolutional, pooling and
fully connected layers [20]. The architecture of CNN is as shown in Fig 3.
3.2.1. Convolutional Layer
In CNN, every neuron in the convolutional layer is linked only to a small portion of the neurons
in the preceding layer, which is in square shape area across the height and width dimensions. The
size of this square is a hyperparameter (controllable parameter) and called as Receptive Field. For
the depth dimension, there is no hyperparameter available since the convolution operations are
normally carried out for the whole depth. Generally, the depth dimension of the input describes
the various colors of the image. Hence it is usually required to link them in order to bring out
necessary information.
Fig.3.The CNN architecture
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
7
Neurons present in the convolution operator can recognize certain local patterns of the previous
layer’s output. Once features are obtained, its actual position is not important and all the neurons
are expected to identify the same pattern. This is achieved by forcing all the neurons to have a
common single set of parameters known as Parameter Sharing [9].
In order to identify various unique features within one layer, it is necessary to have multiple
filters, where each filter is a group of neurons that recognize a certain pattern at different
locations in the image.
3.2.2. Pooling Layer
The main aim of pooling layer is to reduce the complexity of CNNs. The neurons present in the
pooling layer form a square shaped area across the width and height dimensions of the preceding
layer. Even though it is very similar to the convolutional layer, it is different from convolutional
layer because the pooling layer is Non-Parametrized Layer.
The function carried out by this layer is known as subsampling or down sampling. During this
process, contraction in size results in concurrent loss of data. On the other hand, such a loss is
helpful to the network because the reduction in size not only reduces the computational burden
for the succeeding layers of the network and also it reduces the effects of overfitting [12].
Max pooling and average pooling are the generally used techniques shown in (Fig.4). Max
Pooling chooses the largest element within each receptive field [14] whereas Average Pooling
computes the average among the output neurons within the pooling window. Max-pooling
chooses the most prominent feature in a pooling window. On the other hand, average-pooling
method selects whole features into consideration. Thus, max-pooling method keeps texture
related information, while average pooling method retains the background related data [24].
Pooling operation does not combine neurons with different depth values. Instead, the resulting
pooling layer will have the uniform depth as the previous layer and it will only combine local
areas within a filter.
Fig.4.Max Pooling and Average Pooling Operations Illustration.
3.2.3. Fully Connected Layer
The filters and neurons present in this layer are connected to all the activation in the preceding
layers resulting in a completely connected structure. Hence the name. The output feature maps of
the final convolution or pooling layer is converted into a one-dimensional (1D) array of numbers
[18]. High-level reasoning in the network is carried out via fully connected layers [19].
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.12, No.2, March 2021
8
The final fully connected layer has the same number of output nodes as the number of classes.
Each fully connected layer is followed by a nonlinear function, such as ReLU (Rectified Linear
Units). ReLU is an activation function operates by thresholding values at 0, i.e. f (x) = max (0, x).
In other words, it outputs 0 when x < 0, and contrarily, it outputs a linear function with a slope of
1 when x ≥ 0 [15] as shown in the (Fig.5).
Fig.5. The Rectified Linear Unit (ReLU)
3.3. CNN Operation
Based on local receptive field, each component in a convolutional layer accepts inputs from a set
of adjacent units belonging to the preceding layer layer. This way neurons are proficient in
extracting rudimentary features like edges or corners. These features are then linked by the
succeeding convolutional layers in order to further extract high level features.
The components of a convolutional layer are arranged in planes. All units of a plane share the
same set of weights. Thus, each plane is in charge for building a particular feature. The results
obtained from the plane is termed as feature maps. Each convolutional layer consists of several
planes, so that multiple feature maps can be constructed at each location. The most significant
features derived are passed from initial layers to higher layers. As the features are passed to the
higher layer, there is a dimensionality reduction in features determined by kernel size of the
convolutional and max-pooling layers. On the other hand, there is an increase in number of
feature maps for representing better features of the input images for ensuring classification
accuracy [13]. The derived feature vector either could be an input for classification task or could
be treated as a feature vector for next level processing.
The network designed to do the classification task consists of several convolution pooling layers
followed by some fully-connected layers. The first two layers mentioned above perform
convolution and pooling operation in order to extract high-level features. The final layer’s output
of CNN is applied as the input to a fully connected network, which does the task of classification.
The output layer for classification task consists of one neuron for each class and the values of
these neurons describes the score of each class. If score distribution is chosen, the score range
will be between zero and one. The summation of class scores is one. The values of each neuron
can be presumed as the probability of occurrence of the class.
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Fig.6. A visual representation of the various hyperparameters of convolutional layers: receptive field,
stride and padding
4. EXPERIMENTAL SETUP
The implementations are carried out in MATLAB 2019b on a workstation with windows 10 OS,
AMD processor with 2.0 GHz, hard drive with 8 GB RAM. The experiments are conducted in
Extended Yale B and FERET Database with deep CNN model.
4.1. Extended Yale B Database
The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses
and 64 illumination conditions as shown in (Fig.7). The data format of this database is the same
as that of the Yale Face Database B.
Fig.7. Images from extended Yale B database.
4.2. FERET Database
The FERET database was collected in 15 sessions between August 1993 and July 1996. The
database contains 1564 sets of images for a total of 14,126 images that includes 1199 individuals
and 365 duplicate sets of images as shown in (Fig.8). A duplicate set is a second set of images of
a person already in the database and was usually taken on a different day.
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Fig.8. Images from FERET database
4.3. Experiment
The model uses a deep CNN network with six convolution layers (with size 3x3), two
maxpooling and two fully convolutional layers. The first two convolutions use 8 filters, next two
uses 16 and the last convolution layers uses 32 each in order to extract complex features.
The model uses batch normalization and dropouts in all convolutional layers along with ReLU
activation function. In order to reduce the overfitting, the dropout rate is kept higher on the final
convolutional layers. Two maxpooling layers are introduced after second and fourth
convolutional layers to reduce the feature space dimension. The two fully connected layers use
128 and 28 neurons respectively. The final classification layer uses softmax activation with
categorical cross entropy loss function. The softmax layer is used to produce the classification
scores, in which each score is the probability of a particular class for a given instance [31].
The dropout principle is employed on the convolutional neural networks model and the value of
drop out is chosen by trial and error method. The (Fig.9) shows the network model with drop out
and the values of the drop out is different for different layers. The model also uses Adam
Optimizer with a learning rate of 0.001 which was found empirically after trying different
combinations.
The feature maps are subsampled with maxpooling layers with a stride of 2x2. Stride is the
number of pixels which shifts over the input matrix. When the stride is 2, it means the filter is
moved by 2 pixels at a time. The number of neurons in this output layer is limited to 28 in
Extended Yale B database. In FERET database, the number of neurons in the output layer is 994.
Data Augmentation is implemented in the form of simple methods like resizing, rotation, and
reflection. The batch size of 128 and number of epochs 250 are chosen to achieve best results.
The training is designed to use different batch sizes to do a fair trade off between accuracy and
training time.
(a) (b)
Fig.9. (a) The neural networks model (b) The model after applying drop out
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5. RESULTS
The CNN trained with back-propagation algorithm in batch mode with a various batch sizes of 4,
8, 16 and 32 for different epochs are computed for Extended YALE B with an image size of
128×128 for 50 epochs. The same experiment is conducted on FERET database for the frontal
face image size of 128×128 for 300 epochs.
For the Extended YALE B Database, the maximum accuracy rate of 97.2% is achieved for the
batch size of 4 without applying preprocessing technique as shown in (Fig.10). After applying the
preprocessing techniques, the maximum accuracy is improved to 99.8% as shown in (Fig.11).
For the batch size of 8, the maximum accuracy rate of 97.0% is achieved without applying any
preprocessing technique. After applying the preprocessing techniques, the maximum accuracy is
improved to 99.4%.
For the batch size of 8, SQI, LTISN and HE perform very equally well and yield the maximum
accuracy of 99.4%, 99.3% and 99.1%.
For the batch size of 16, the maximum accuracy rate of 96.8% is achieved without applying
preprocessing technique. After applying the preprocessing techniques, the maximum accuracy is
improved to 99.1%.
For the batch size of 16 HE, SQI and GIC perform equally well and yield the maximum accuracy
of 99.1%. 98.8% and 98.8% respectively.
For the batch size of 32, the maximum accuracy rate of 96.2% is achieved without applying
preprocessing technique. After applying the preprocessing techniques, the maximum accuracy is
improved to 98.7% for the same batch size.
For the batch size of 32, GIC, SQI, and HE perform equally well and yield the maximum
accuracy of 98.7%, 98.3% and 98.3% respectively. Table 1 shows the accuracy rates of Extended
Yale B database for various batch sizes before and after application of preprocessing techniques.
For the FERET Database, the maximum accuracy rate of 71.4% is achieved without applying
preprocessing technique, for the batch size of 4, as shown in (Fig.12). After applying the
preprocessing techniques, the maximum accuracy improved to 76.6% as shown in (Fig.13).
DoG, HE and LTISN perform equally well and yield the maximum accuracy of 76.6%, 76.3%
and 75.6% respectively.
For the batch size of 8, the maximum accuracy rate of 71.2% is achieved without applying
preprocessing technique. After applying the preprocessing techniques, the maximum accuracy is
improved to 76.4%.
For the batch size of 8, HE, SQI and LTISN perform very equally well and yield the maximum
accuracy of 76.4%, 74.6% and 72.5% respectively.
For the batch size of 16, the maximum accuracy rate of 71.0% is achieved without applying
preprocessing technique. After applying the preprocessing techniques, the maximum accuracy is
improved to 76.1%.
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For the batch size of 16 HE, LTISN and SQI perform very equally well and yield the maximum
accuracy of 76.1%, 72.0% and 71.5% respectively.
For the batch size of 32, the maximum accuracy rate of 68.7% is achieved without applying
preprocessing technique. After applying the preprocessing techniques, the maximum accuracy is
improved to 71.0%.
For the batch size of 32, SQI, GIC, LTISN and DoG perform equally well and yield the
maximum accuracy of 71%, 70.9%, 70.8% and 69.9% respectively.
SQI and HE perform equally well and provide best results for all the batch sizes in both Extended
Yale B and FERET database.
Fig.10.Accuracy using Extended YALE B Database before applying of preprocessing techniques
Fig.11.Accuracy using Extended YALE B Database after application of preprocessing techniques
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Fig.12.Accuracy using FERET Database before the application of preprocessing techniques.
Fig.13.Accuracy using FERET Database after application of preprocessing techniques.
Table 1 Accuracy rates of Extended Yale B database for various batch sizes before and after application
of preprocessing techniques.
Database Accuracy Rates
Batch Size
4 8 16 32
Extended Yale B (WOAPP) 97.2 97.0 96.8 96.2
DoG 99.1 99.0 98.7 97.9
SQI 99.8 99.4 98.8 98.3
LTISN 99.2 99.3 98.4 98.1
HE 99.7 99.1 99.1 98.3
GIC 99.6 98.9 98.8 98.7
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Table 2 Accuracy rates of FERET database for various batch sizes before and after application
of preprocessing techniques
Database AccuracyRates
Batch Size
4 8 16 32
FERET(WOAPP) 71.4 71.2 71 68.7
DoG 76.6 72.6 71.3 69.9
SQI 74.9 74.6 71.5 71.0
LTISN 75.6 72.5 72.0 70.8
HE 76.3 76.4 76.1 69.2
GIC 74.2 71.3 71.0 70,9
Data augmentation with the batch size of 128 and number of epochs 250 yields best results,
especially with the preprocessing technique Histogram Equalization. In FERET Database, the
maximum accuracy of 79.6% is achieved.
For the same batch size and number of epochs, LTISN provides almost the same accuracy rate of
78.9%.
DoG, SQI and GIC performs almost equally and provides the accuracy enhancement of 78.2%,
78.3% and 78.1%
Data augmentation Methods does not have much effect in Extended Yale B database. With the
batch size of 128 and number of epochs 250 yields best results, especially with the preprocessing
technique of Histogram Equalization. In Extended Yale B Database, the maximum accuracy of
99.86% is achieved.
For the same batch size and number of epochs, SQI and LTISN provides almost the same
accuracy rate of 99.91% and 99.31%
DoG, HE and GIC performs almost equally and provides the accuracy enhancement of 99.21%,
99.78% and 99.71%
5.1. Number of trainable parameters calculation in Model
The number of trainable parameters is computed for each layer as below. The deep architecture is
as shown in (Fig.14).
C1 Layer
Input image 128×128×1 and having one channel. Number of weights per filter=3×3×1
There are totally eight filters in C1 layer and there is one bias parameter for each filter.
Total number of trainable parameters in C1 Layer is = (3×3×1+1) × 8= 80.
C2 Layer
Input feature maps to C2 layer having 8 channels. Number of weights per filter is 3×3×8.
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There are 8 such filters in the layer. Hence total number of weights = ((3×3×8) +1) ×8 and there
is one bias for each filter.
Total weights in C2 = ((3×3×8) +1) ×12=584.
S1 Layer
S1 layer is using max pooling operation. Hence no trainable parameter available for this layer.
C3 Layer
Input to C3 has eight feature channels. Number of weights per filter is 3×3×8.
There are 16 such filters in the layer. Hence total number of weights = ((3×3) ×8+1) ×16 and
there is one bias for each filter.
Total weights in C3 = (3×3×8+1) ×16=1168.
C4 Layer
Input to C4 has sixteen feature channels. Number of weights per filter is 3×3×16.
There are 16 such filters in the layer. Hence total number of weights = ((3×3) ×16+1) ×16 and
there is one bias for each filter.
Total weights in C4 = (3×3×16+1) ×16=2320.
S2 Layer
S2 layer is using max pooling operation. Hence no trainable parameter available for this layer.
C5 Layer
Input to C5 has sixteen feature channels. Number of weights per filter is 3×3×16.
There are 32 such filters in the layer. Hence total number of weights = ((3×3) ×16+1) ×32 and
there is one bias for each filter.
Total weights in C4 = (3×3×16+1) ×32=4640.
Fig.14. Deep CNN Model Architecture (BN-Batch Normalization)
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C6 Layer
Input to C6 has 32 feature channels. Number of weights per filter is 3×3×32
There are 32 such filters in the layer. Hence total number of weights = (3×3 ×32+1) ×32 and
there is one bias for each filter.
Total weights in C4 = (3×3×32+1) ×32=9248.
F1 Layer
In the fully connected layer, the input is having total feature space of size 32×32 and 32. Input
samples to F1 layer=32×32×32
There are 128 neurons in F1 layer and each have a bias.
Total Number of trainable parameters for F1 layer is = (32×32×2+1) ×128=4194432.
Fig.15.Feature map obtained for the batch size 32 and epoch 2
Fig.16.Feature map obtained for the batch size 32 and epoch 5
Fig.17.Feature map obtained for the batch size 32 and epoch 25
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F2 Layer
The input to F2 layer is the output from F1 layer. It is connected to 28 neurons (since there are 28
classes). So, total number of trainable parameters = (128 +1) ×28= 16512.
Feature maps extracted for different batch size and epochs are as shown in the (Fig.15,16,17).
6. COMPARISON WITH OTHER APPROACHES
In this section, the proposed approach is compared with the various approaches.
Hybrid system, which combines convolutional neural network (CNN) and a Logistic regression
classifier (LRC) yields the accuracy of 80%. A CNN is trained to recognize facial images and
LRC is used to classify the features learned by the convolutional network [26]. The neural
network with the nonlinear theories, such as wavelet theory and fuzzy set is a new research idea,
when applied in face recognition, gives accuracy around 93% in Wavelet-BP [25]. CNN
architecture along with Max-Feature-Map (MFM) could extract compact vital information yields
the accuracy rate of 98% [28]. The popular LeNet-5 architecture in FERET database yields
accuracy rate is 81.25% [27]. The pre-trained CNN model along with the VGG- Face provides
face verification rate accuracy of 86.85% and 83.32% on FRGC and LFW databases respectively
[29].
Table 3. Comparison of Accuracy rates of different approaches.
Sno Architectureused Dataset Accuracy
1 Proposedmethod(C
NN)
Extended Yale
BFERET
99.8%
76.3%
2 VGG+CNN[29] FRGC,LFW 86.85%,
83.32%
3 CNN withMAF
activationfunction
[28]
LFW 98%
4 Wavelet-BP[25] AT&T 93.0%
5 CNN-LRC
[26]
Yale 80%
6 CNN-LENET[27] FERET 81.25%
The proposed method compared with existing approaches are given in Table 3. The proposed
method performs better in comparison to all other approaches in Extended Yale B. In FERET
database, CNN-LENET method performs better than the proposed method.
7. CONCLUSION
In this paper, deep convolution neural network (CNN) is applied for extracting features and
classification. The performance of the network is assessed before and after applying
preprocessing techniques for various batch sizes and epochs on YALE B and FERET dataset.
Adam optimizers with a learning rate of 0.001 is applied in this experiment. The batch sizes used
in this experiment are [4, 8, 16,32];
The optimum batch sizes are chosen using trial and error method. Initially, multiple batch sizes of
[4,8,16,32,64,128] are selected for this experiment. Batch size of 4 with learning rate of0.001 and
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number of epochs 50 give the best results. Batch sizes of [4,8,16,32] provide consistent and best
results. The batch sizes are normally selected in power of 2. Choosing smaller batch size with
low value of learning rate yields best result.
The results showed the maximum accuracy rate of 97.2% of achieved without using
preprocessing techniques. When the preprocessing techniques are applied, the improved accuracy
rates are achieved up to 99.8%.
The same experiments are conducted in FERET. dataset also. For the frontal face, the maximum
71.4% is achieved without optimization. After applying the preprocessing techniques, the
accuracy rate increased to 76.3%.
The proposed approach performs very well as compared to existing methods.
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