This document describes a deep learning approach for facial in-painting to generate plausible facial structures for missing pixels in face images. The approach uses the observable region of a face as well as inferred attributes like gender, ethnicity, and expression. It selects a guidance face matching the attributes to in-paint missing areas like the mouth or eyes. In-painting is done on intrinsic image layers instead of RGB to handle illumination differences. The proposed method uses face detection, alignment, representation, and matching with real-time datasets to fill missing or masked regions with realistic synthesized content.
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
Progression in Large Age-Gap Face VerificationIRJET Journal
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.
A Novel Approach of Fuzzy Based Semi-Automatic Annotation for Similar Facial ...ijsrd.com
This paper proposes a semi-automatic approach for annotating similar facial images that are often weakly labeled with duplicate, noisy, or incomplete names. It uses an unsupervised label refinement (ULR) algorithm with fuzzy clustering to improve the labels. The ULR algorithm refines the labels through multiple iterations using machine learning techniques. It also uses a parallel computation framework to solve very large problems efficiently. Evaluation on a dataset with introduced noise shows the proposed optimized fuzzy ULR approach outperforms other ULR algorithms in refining the labels.
Gender classification using custom convolutional neural networks architecture IJECEIAES
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.
The document describes a proposed method for facial expression recognition in videos using 3D convolutional neural networks and long short-term memory. Specifically, it proposes a 3D Inception-ResNet architecture to extract both spatial and temporal features from video sequences. It also incorporates facial landmarks to emphasize important facial components. The landmarks are used to generate filters during training. Finally, an LSTM unit is used to further extract temporal information from the enhanced feature maps output by the 3D Inception-ResNet layers. The proposed method is evaluated on several facial expression databases and is shown to outperform state-of-the-art methods.
IRJET - Factors Affecting Deployment of Deep Learning based Face Recognition ...IRJET Journal
This document discusses factors affecting the deployment of deep learning models for face recognition on smartphones. It examines training data requirements, suitable neural network architectures, and effective loss functions. Larger datasets with more subjects and images are preferred for training models that generalize well. Residual networks like ResNet have achieved good accuracy while being efficient for face recognition. Loss functions like center loss and triplet loss help learn discriminative features by reducing intra-class and increasing inter-class variations.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
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
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
Progression in Large Age-Gap Face VerificationIRJET Journal
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.
A Novel Approach of Fuzzy Based Semi-Automatic Annotation for Similar Facial ...ijsrd.com
This paper proposes a semi-automatic approach for annotating similar facial images that are often weakly labeled with duplicate, noisy, or incomplete names. It uses an unsupervised label refinement (ULR) algorithm with fuzzy clustering to improve the labels. The ULR algorithm refines the labels through multiple iterations using machine learning techniques. It also uses a parallel computation framework to solve very large problems efficiently. Evaluation on a dataset with introduced noise shows the proposed optimized fuzzy ULR approach outperforms other ULR algorithms in refining the labels.
Gender classification using custom convolutional neural networks architecture IJECEIAES
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.
The document describes a proposed method for facial expression recognition in videos using 3D convolutional neural networks and long short-term memory. Specifically, it proposes a 3D Inception-ResNet architecture to extract both spatial and temporal features from video sequences. It also incorporates facial landmarks to emphasize important facial components. The landmarks are used to generate filters during training. Finally, an LSTM unit is used to further extract temporal information from the enhanced feature maps output by the 3D Inception-ResNet layers. The proposed method is evaluated on several facial expression databases and is shown to outperform state-of-the-art methods.
IRJET - Factors Affecting Deployment of Deep Learning based Face Recognition ...IRJET Journal
This document discusses factors affecting the deployment of deep learning models for face recognition on smartphones. It examines training data requirements, suitable neural network architectures, and effective loss functions. Larger datasets with more subjects and images are preferred for training models that generalize well. Residual networks like ResNet have achieved good accuracy while being efficient for face recognition. Loss functions like center loss and triplet loss help learn discriminative features by reducing intra-class and increasing inter-class variations.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
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
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
Performance Comparison of Face Recognition Using DCT Against Face Recognition...CSCJournals
In this paper, a face recognition system using simple Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is Georgia Tech Face Database which contains color JPEG images, all are of different size. Another database used for experimental purpose is Indian Face Database. It contains color bitmap images. Using above VQ techniques, codebooks of different size are generated and recognition rate is calculated for each codebook size. This recognition rate is compared with the one obtained by applying DCT on image and LBG-VQ algorithm which is used as benchmark in vector quantization. Results show that KFCG outperforms other three VQ techniques and gives better recognition rate up to 85.4% for Georgia Tech Face Database and 90.66% for Indian Face Database. As no Euclidean distance computations are involved in KMCG and KFCG, they require less time to generate the codebook as compared to LBG and KPE
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Deep learning for pose-invariant face detection in unconstrained environmentIJECEIAES
In the recent past, convolutional neural networks (CNNs) have seen resurgence and have performed extremely well on vision tasks. Visually the model resembles a series of layers each of which is processed by a function to form a next layer. It is argued that CNN first models the low level features such as edges and joints and then expresses higher level features as a composition of these low level features. The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, the probabilistic measure of the similarity of the face images will be done using Bayesian analysis. Experiment detects faces with ±90 degree out of plane rotations. Fine tuned AlexNet is used to detect pose invariant faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.
This document provides information about Elysium Technologies Private Limited, an ISO 9001:2008 certified research and development company located in Singapore, Madurai, Trichy, Coimbatore, Kollam, and Cochin. It lists their branch office locations and contact information. The document then provides a list of 12 digital image processing projects available for the 2012-2013 academic year.
IRJET- Spot Me - A Smart Attendance System based on Face RecognitionIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
IRJET- Design of an Automated Attendance System using Face Recognition AlgorithmIRJET Journal
This document describes the design of an automated attendance system using face recognition for Nigerian universities. It aims to provide an efficient alternative to the problematic paper-based attendance system. The proposed system uses single scale Retinex with bi-histogram equalization to enhance face images captured by a webcam. Then, the Viola-Jones algorithm is used to detect faces, and principal component analysis (PCA) is used to recognize faces by comparing them to template images stored in a database. The system was able to achieve over 90% accuracy in tests. If a captured face matches a stored template, a '1' is recorded to mark the student as present. Otherwise, a '0' is recorded to mark them as absent. The percentage
This document summarizes a research paper that proposes a robust image alignment method for tampering detection using SVM clustering of bag of features. The method first extracts bag of features from an image and clusters them using SVM clustering for effective image alignment. An image hash based on the bag of features is attached to images before transmission. The hash is then analyzed at the destination to detect any geometric transformations applied to the received image and align it to the original for tampering detection. The proposed approach provides good performance compared to state-of-the-art methods in localizing tampered regions through robust image alignment.
IRJET - A Review on Face Recognition using Deep Learning AlgorithmIRJET Journal
This document provides an overview of face recognition using deep learning algorithms. It discusses how deep learning approaches like convolutional neural networks (CNNs) have achieved high accuracy in face recognition tasks compared to earlier methods. CNNs can learn discriminative face features from large datasets during training to generalize to new images, handling variations in pose, illumination and expression. The document reviews popular CNN architectures and training approaches for face recognition. It also discusses other traditional face recognition methods like PCA and LDA, and compares their performance to deep learning methods.
This document summarizes recent developments in face recognition technology. It discusses how face recognition can be used for verification (one-to-one matching) or identification (one-to-many matching). It then outlines several major approaches to face recognition including template-based methods, statistical approaches, and neural network-based approaches. Specific algorithms are discussed, such as Viola-Jones, HOG, R-CNN, and YOLO. Finally, potential applications of face recognition technology are presented, including biometrics, healthcare, education, and emotion analysis.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Visual Saliency Model Using Sift and Comparison of Learning Approachescsandit
This document discusses a study that aims to develop a visual saliency model to predict where humans look in images. It uses the SIFT feature in addition to low, mid, and high-level image features to train machine learning models on an eye-tracking dataset. Support vector machines (SVM) achieved the best performance, accurately predicting fixations 88% of the time. Including the SIFT feature further improved SVM performance to 91% accuracy. The study evaluates different machine learning methods and determines SVM to be best suited for this binary classification task using high-dimensional image data.
This document summarizes a research paper on face recognition that accounts for variations in pose, alignment, color, illumination, and expression. The paper proposes using a Pyramid Convolutional Neural Network (Pyramid CNN) which can learn face representations in a computationally efficient manner. The Pyramid CNN incorporates feature sharing across multi-scale face representations to increase discriminative ability. It also uses 3D alignment and color normalization to reduce the effects of pose, lighting, and color differences. Evaluation on the LFW database shows the Pyramid CNN approach achieves competitive recognition accuracy.
ADVANCED FACE RECOGNITION FOR CONTROLLING CRIME USING PCAIAEME Publication
Face recognition has been a rapidly creating, testing and fascinating area with
respect to consistent applications. The task of face acknowledgment has been viably
asked about lately. With data and information gathering in abundance, there is an
urgent necessity for high security. Face acknowledgment has been a rapidly creating,
testing and interesting area concerning persistent applications. This paper gives a
cutting edge review of critical human face acknowledgment investigate
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
IRJET- Prediction of Facial Attribute without Landmark InformationIRJET Journal
This document summarizes a research paper on predicting facial attributes without using landmark information. It describes the AFFAIR method which uses a global transformation network and part localization network to predict attributes. The global network learns an optimized transformation for each input face for attribute prediction. The part network localizes the most relevant facial regions for specific attributes. The global and local representations are then fused to predict attributes with high accuracy without requiring external landmark points. Experimental results on standard datasets show the AFFAIR method achieves state-of-the-art performance in landmark-free facial attribute prediction.
IRJET - Gender and Age Prediction using Wideresnet ArchitectureIRJET Journal
This document describes a gender and age prediction system using the WideResnet convolutional neural network architecture. The system is trained on the IMDB dataset containing over 500,000 images of faces with labeled gender and age information. The proposed system takes an input face image, passes it through the WideResnet model to classify the gender as male or female and estimate an age range. WideResnet is chosen to improve the performance and accuracy of existing gender and age prediction systems by reducing issues caused by lighting conditions and image capture angles. The system is implemented using TensorFlow and Keras frameworks and evaluated on the IMDB test dataset.
In this paper, an attempt has been made to extract texture
features from facial images using an improved method of
Illumination Invariant Feature Descriptor. The proposed local
ternary Pattern based feature extractor viz., Steady Illumination
Local Ternary Pattern (SIcLTP) has been used to extract texture
features from Indian face database. The similarity matching
between two extracted feature sets has been obtained using Zero
Mean Sum of Squared Differences (ZSSD). The RGB facial images
are first converted into the YIQ colour space to reduce the
redundancy of the RGB images. The result obtained has been
analysed using Receiver Operating Characteristic curve, and is
found to be promising. Finally the results are validated with
standard local binary pattern (LBP) extractor.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONijaia
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
FACE PHOTO-SKETCH RECOGNITION USING DEEP LEARNING TECHNIQUES - A REVIEWIRJET Journal
This document reviews various deep learning techniques for face photo-sketch recognition. It summarizes several studies that used deep learning and other machine learning methods to match faces across photo and sketch domains. Feature injection and convolutional neural networks, knowledge distillation frameworks, relational graph modules, domain alignment embedding networks, and using 3D morphable models and CNNs for forensic sketch recognition are some of the techniques discussed. The studies aim to improve cross-domain face recognition accuracy, which remains challenging due to differences in photo and sketch representations. Accuracies ranging from 73-95% are reported for the various methods.
Image super resolution using Generative Adversarial Network.IRJET Journal
This document discusses using a generative adversarial network (GAN) for image super resolution. It begins with an abstract that explains super resolution aims to increase image resolution by adding sub-pixel detail. Convolutional neural networks are well-suited for this task. Recent years have seen interest in reconstructing super resolution video sequences from low resolution images. The document then reviews literature on image super resolution techniques including deep learning methods. It describes the methodology which uses a CNN to compare input images to a trained dataset to predict if high-resolution images can be generated from low-resolution images.
Performance Comparison of Face Recognition Using DCT Against Face Recognition...CSCJournals
In this paper, a face recognition system using simple Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is Georgia Tech Face Database which contains color JPEG images, all are of different size. Another database used for experimental purpose is Indian Face Database. It contains color bitmap images. Using above VQ techniques, codebooks of different size are generated and recognition rate is calculated for each codebook size. This recognition rate is compared with the one obtained by applying DCT on image and LBG-VQ algorithm which is used as benchmark in vector quantization. Results show that KFCG outperforms other three VQ techniques and gives better recognition rate up to 85.4% for Georgia Tech Face Database and 90.66% for Indian Face Database. As no Euclidean distance computations are involved in KMCG and KFCG, they require less time to generate the codebook as compared to LBG and KPE
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Deep learning for pose-invariant face detection in unconstrained environmentIJECEIAES
In the recent past, convolutional neural networks (CNNs) have seen resurgence and have performed extremely well on vision tasks. Visually the model resembles a series of layers each of which is processed by a function to form a next layer. It is argued that CNN first models the low level features such as edges and joints and then expresses higher level features as a composition of these low level features. The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, the probabilistic measure of the similarity of the face images will be done using Bayesian analysis. Experiment detects faces with ±90 degree out of plane rotations. Fine tuned AlexNet is used to detect pose invariant faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.
This document provides information about Elysium Technologies Private Limited, an ISO 9001:2008 certified research and development company located in Singapore, Madurai, Trichy, Coimbatore, Kollam, and Cochin. It lists their branch office locations and contact information. The document then provides a list of 12 digital image processing projects available for the 2012-2013 academic year.
IRJET- Spot Me - A Smart Attendance System based on Face RecognitionIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
IRJET- Design of an Automated Attendance System using Face Recognition AlgorithmIRJET Journal
This document describes the design of an automated attendance system using face recognition for Nigerian universities. It aims to provide an efficient alternative to the problematic paper-based attendance system. The proposed system uses single scale Retinex with bi-histogram equalization to enhance face images captured by a webcam. Then, the Viola-Jones algorithm is used to detect faces, and principal component analysis (PCA) is used to recognize faces by comparing them to template images stored in a database. The system was able to achieve over 90% accuracy in tests. If a captured face matches a stored template, a '1' is recorded to mark the student as present. Otherwise, a '0' is recorded to mark them as absent. The percentage
This document summarizes a research paper that proposes a robust image alignment method for tampering detection using SVM clustering of bag of features. The method first extracts bag of features from an image and clusters them using SVM clustering for effective image alignment. An image hash based on the bag of features is attached to images before transmission. The hash is then analyzed at the destination to detect any geometric transformations applied to the received image and align it to the original for tampering detection. The proposed approach provides good performance compared to state-of-the-art methods in localizing tampered regions through robust image alignment.
IRJET - A Review on Face Recognition using Deep Learning AlgorithmIRJET Journal
This document provides an overview of face recognition using deep learning algorithms. It discusses how deep learning approaches like convolutional neural networks (CNNs) have achieved high accuracy in face recognition tasks compared to earlier methods. CNNs can learn discriminative face features from large datasets during training to generalize to new images, handling variations in pose, illumination and expression. The document reviews popular CNN architectures and training approaches for face recognition. It also discusses other traditional face recognition methods like PCA and LDA, and compares their performance to deep learning methods.
This document summarizes recent developments in face recognition technology. It discusses how face recognition can be used for verification (one-to-one matching) or identification (one-to-many matching). It then outlines several major approaches to face recognition including template-based methods, statistical approaches, and neural network-based approaches. Specific algorithms are discussed, such as Viola-Jones, HOG, R-CNN, and YOLO. Finally, potential applications of face recognition technology are presented, including biometrics, healthcare, education, and emotion analysis.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Visual Saliency Model Using Sift and Comparison of Learning Approachescsandit
This document discusses a study that aims to develop a visual saliency model to predict where humans look in images. It uses the SIFT feature in addition to low, mid, and high-level image features to train machine learning models on an eye-tracking dataset. Support vector machines (SVM) achieved the best performance, accurately predicting fixations 88% of the time. Including the SIFT feature further improved SVM performance to 91% accuracy. The study evaluates different machine learning methods and determines SVM to be best suited for this binary classification task using high-dimensional image data.
This document summarizes a research paper on face recognition that accounts for variations in pose, alignment, color, illumination, and expression. The paper proposes using a Pyramid Convolutional Neural Network (Pyramid CNN) which can learn face representations in a computationally efficient manner. The Pyramid CNN incorporates feature sharing across multi-scale face representations to increase discriminative ability. It also uses 3D alignment and color normalization to reduce the effects of pose, lighting, and color differences. Evaluation on the LFW database shows the Pyramid CNN approach achieves competitive recognition accuracy.
ADVANCED FACE RECOGNITION FOR CONTROLLING CRIME USING PCAIAEME Publication
Face recognition has been a rapidly creating, testing and fascinating area with
respect to consistent applications. The task of face acknowledgment has been viably
asked about lately. With data and information gathering in abundance, there is an
urgent necessity for high security. Face acknowledgment has been a rapidly creating,
testing and interesting area concerning persistent applications. This paper gives a
cutting edge review of critical human face acknowledgment investigate
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
IRJET- Prediction of Facial Attribute without Landmark InformationIRJET Journal
This document summarizes a research paper on predicting facial attributes without using landmark information. It describes the AFFAIR method which uses a global transformation network and part localization network to predict attributes. The global network learns an optimized transformation for each input face for attribute prediction. The part network localizes the most relevant facial regions for specific attributes. The global and local representations are then fused to predict attributes with high accuracy without requiring external landmark points. Experimental results on standard datasets show the AFFAIR method achieves state-of-the-art performance in landmark-free facial attribute prediction.
IRJET - Gender and Age Prediction using Wideresnet ArchitectureIRJET Journal
This document describes a gender and age prediction system using the WideResnet convolutional neural network architecture. The system is trained on the IMDB dataset containing over 500,000 images of faces with labeled gender and age information. The proposed system takes an input face image, passes it through the WideResnet model to classify the gender as male or female and estimate an age range. WideResnet is chosen to improve the performance and accuracy of existing gender and age prediction systems by reducing issues caused by lighting conditions and image capture angles. The system is implemented using TensorFlow and Keras frameworks and evaluated on the IMDB test dataset.
In this paper, an attempt has been made to extract texture
features from facial images using an improved method of
Illumination Invariant Feature Descriptor. The proposed local
ternary Pattern based feature extractor viz., Steady Illumination
Local Ternary Pattern (SIcLTP) has been used to extract texture
features from Indian face database. The similarity matching
between two extracted feature sets has been obtained using Zero
Mean Sum of Squared Differences (ZSSD). The RGB facial images
are first converted into the YIQ colour space to reduce the
redundancy of the RGB images. The result obtained has been
analysed using Receiver Operating Characteristic curve, and is
found to be promising. Finally the results are validated with
standard local binary pattern (LBP) extractor.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONijaia
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
FACE PHOTO-SKETCH RECOGNITION USING DEEP LEARNING TECHNIQUES - A REVIEWIRJET Journal
This document reviews various deep learning techniques for face photo-sketch recognition. It summarizes several studies that used deep learning and other machine learning methods to match faces across photo and sketch domains. Feature injection and convolutional neural networks, knowledge distillation frameworks, relational graph modules, domain alignment embedding networks, and using 3D morphable models and CNNs for forensic sketch recognition are some of the techniques discussed. The studies aim to improve cross-domain face recognition accuracy, which remains challenging due to differences in photo and sketch representations. Accuracies ranging from 73-95% are reported for the various methods.
Image super resolution using Generative Adversarial Network.IRJET Journal
This document discusses using a generative adversarial network (GAN) for image super resolution. It begins with an abstract that explains super resolution aims to increase image resolution by adding sub-pixel detail. Convolutional neural networks are well-suited for this task. Recent years have seen interest in reconstructing super resolution video sequences from low resolution images. The document then reviews literature on image super resolution techniques including deep learning methods. It describes the methodology which uses a CNN to compare input images to a trained dataset to predict if high-resolution images can be generated from low-resolution images.
IRJET - Deep Learning Approach to Inpainting and Outpainting SystemIRJET Journal
This document discusses a deep learning approach for image inpainting and outpainting. It proposes a new generative model-based approach using a fully convolutional neural network that can process images with multiple holes at variable locations and sizes. The model aims to not only synthesize novel image structures, but also explicitly utilize surrounding image features as references during training to generate better predictions. Experiments on faces, textures and natural images demonstrate the proposed approach generates higher quality inpainting results than existing methods. It aims to address limitations of CNNs in borrowing information from distant areas by leveraging texture and patch synthesis approaches.
FaceDetectionforColorImageBasedonMATLAB.pdfAnita Pal
The document proposes a method for face detection in color images using MATLAB and a convolutional neural network (CNN) with two layers. It begins with an introduction to face detection technologies and challenges. It then describes the proposed methodology, which involves reading a color image, applying CNN convolutions to detect faces, and fusing the results. Mathematical aspects of CNNs are discussed, including equations for convolutional operations and dimensions. The CNN method combines feature extraction, segmentation, and classification. Finally, the document summarizes how the proposed algorithm was designed in MATLAB to identify faces using a simple learning algorithm and CNNs with fewer layers than traditional networks. The goal is to develop a fast face detection program for color images based on new deep learning standards.
IRJET- Face Recognition of Criminals for Security using Principal Component A...IRJET Journal
This document presents a face recognition system using principal component analysis to identify criminals at airports. The system is trained on images of known criminals collected from law enforcement agencies. It uses PCA for dimensionality reduction to generate eigenfaces from the training images. During testing, it generates an eigenface from the input image and calculates the Euclidean distance between this eigenface and the eigenfaces of the training images. It identifies the criminal as the one corresponding to the training image with the minimum distance, alerting authorities. The document outlines the methodology, including preprocessing steps like subtracting the mean face, and reviews prior work applying PCA and other algorithms to face recognition.
IRJET- Face Recognition using Machine LearningIRJET Journal
This document presents a modified CNN architecture for face recognition that adds two batch normalization operations to improve performance. The CNN extracts facial features using convolutional layers and max pooling, and classifies faces using a softmax classifier. The proposed approach was tested on a face database containing images of 4 individuals with varying lighting conditions. Experimental results showed the modified CNN with batch normalization achieved better recognition results than traditional methods.
Deep learning methods have improved machine capabilities for face recognition. Three key modules are typically used: 1) A face detector localizes faces in images using region-based or sliding window approaches with DCNNs. 2) A fiducial point detector localizes facial landmarks using DCNN regressors or 3D models. 3) A DCNN extracts features for face recognition and verification by computing similarity scores between representations. Large annotated datasets have enabled DCNNs to learn robust representations for unconstrained images.
IRJET- An Improvised Multi Focus Image Fusion Algorithm through QuadtreeIRJET Journal
The document proposes a new quadtree-based algorithm for multi-focus image fusion. The algorithm divides the input images into 4 equal blocks using a quadtree structure. It then further divides each block into smaller blocks and detects the focused regions in each block using a focus measure and weighted values. The small blocks are then fused using a modified Laplacian mechanism. The fused image is evaluated using SSIM and ESSIM values, which indicate the proposed algorithm performs better fusion than previous methods.
MUSIC RECOMMENDATION THROUGH FACE RECOGNITION AND EMOTION DETECTIONIRJET Journal
This document describes a system for music recommendation based on facial emotion recognition using convolutional neural networks. It analyzes facial images using CNN models to detect seven basic emotions. Based on the detected emotion, it recommends songs from predefined playlists that match the user's mood. The system architecture inputs images of the user's face, uses a CNN for emotion detection, and then selects appropriate music from playlists organized by emotion. It was developed to provide personalized music recommendations based on a user's real-time facial expressions and emotional state, unlike other systems that rely only on search queries or prior listening history.
Review Paper on Attendance Capturing System using Face RecognitionIRJET Journal
This document summarizes research on various attendance capturing systems using face recognition. It reviews 9 research papers describing different approaches using techniques like convolutional neural networks, MTCNN algorithm, Haar cascade classifier, and PCA. These systems are able to automate attendance marking by detecting faces in images and videos and recognizing students in real-time with accuracy rates ranging from 56% to 99.86%. The reviewed systems provide benefits over manual attendance methods by saving time while also being more accurate in some cases.
Efficient resampling features and convolution neural network model for image ...IJEECSIAES
The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.
Efficient resampling features and convolution neural network model for image ...nooriasukmaningtyas
The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.
A Literature Survey on Image Linguistic Visual Question AnsweringIRJET Journal
This document discusses a literature survey on image and linguistic visual question answering. It aims to develop a model that achieves higher performance than state-of-the-art solutions by exploring different existing models and developing a custom model. The paper reviews several existing models for visual question answering and image classification using convolutional neural networks. It also discusses developing a new dataset for visual question answering using automated question generation from image descriptions.
IRJET - Facial Recognition based Attendance System with LBPHIRJET Journal
This document presents a facial recognition based attendance system using LBPH (Local Binary Pattern Histograms). It begins with an abstract describing the system which takes student attendance using facial identification from classroom camera images. It then discusses related work in attendance and face recognition systems. The proposed system workflow is described involving face detection, feature extraction using LBPH, template matching, and attendance recording. Experimental results demonstrate the system's ability to detect multiple faces and record attendance accurately in an Excel sheet with date/time. The conclusion discusses how the system reduces human effort for attendance and increases learning time compared to traditional methods.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
Real time multi face detection using deep learningReallykul Kuul
This document proposes a framework for real-time multiple face recognition using deep learning on an embedded GPU system. The framework includes face detection using a CNN, face tracking to reduce processing time, and a state-of-the-art deep CNN for face recognition. Experimental results showed the system can recognize up to 8 faces simultaneously in real-time, with processing times up to 0.23 seconds and a minimum recognition rate of 83.67%.
Facial recognition based on enhanced neural networkIAESIJAI
Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.
Analysis of student sentiment during video class with multi-layer deep learni...IJECEIAES
The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the online class is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
Most face recognition algorithms are generally capable to achieve a high level of accuracy when
the image is acquired under wellcontrolled conditions. The face should be still during the acquisition
process; otherwise, the resulted image would be blur and hard for recognition. Enforcing persons to stand
still during the process is impractical; extremely likely that recognition should be performed on a blurred
image. It is important to understand the relation between the image blur and the recognition accuracy. The
ORL Database was used in the study. All images were in PGM format of 92 × 112 pixels from forty
different persons, ten images per person. Those images were randomly divided into training and testing
datasets with 50-50 ratio. Singular value decomposition was used to extract the features. The images in
the testing datasets were artificially blurred to represent a linear motion, and recognition was performed.
The blurred images were also filtered using various methods. The accuracy levels of the recognition on the
basis of the blurred faces and filtered faces were compared. The performed numerical study suggests that
at its best, the image improvement processes are capable to improve the recognition accuracy level by
less than five percent.
Face Recognition Smart Attendance System- A SurveyIRJET Journal
This document surveys 15 research papers on face recognition smart attendance systems. It summarizes each paper's methodology, including the databases and images used, feature extraction and matching algorithms like PCA, LDA, CNN, techniques for addressing issues like lighting and pose variations, and the accuracy and limitations of each system. Overall, the papers presented a variety of approaches to developing face recognition systems for automated student attendance, comparing methods like PCA, LDA, HOG, and deep learning algorithms and evaluating factors like recognition rate, robustness, and speed.
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STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
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Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
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A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
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Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
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Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
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A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
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React based fullstack edtech web applicationIRJET Journal
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Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
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Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.