Image Face Recognition Research Articles

Face Image Recognition by using some Dimension Reduction Algorithms and Logistic Regression

Face recognition is a common technique for artificial intelligence and image processing in which machine-learning algorithms automatically recognize a person's face through a huge image collection or even from a live video since there is difficulty in detecting data when using different shots for the same person in a facial recognition system, it takes time to determine person's membership. Furthermore, when working with high-dimensional data, where the 2D-pixel values of facial images, can be computationally challenging, the face recognition problem gets harder, so the current research compares the dimensionality reduction techniques like the principal component analysis algorithm PCA, an unsupervised learning algorithm that reduces dimensions by moving them from a high-dimensional area to a lower area without losing important information, and the two-dimensional principal component analysis algorithm 2DPCA to determine which technique is most effective for the process of detecting facial images, and this method will be compared with the logistic regression which is a statistical model for face recognition that has been used for classification tasks. Two different sets of data were used, the first group represented the ORL database, which was made up of 40 individuals, while the second group represented the real data, which was made up of 100 individuals from AL-Mamoon College, the results showed that the logistic regression model achieved the lowest MSE for ORL dataset and real data by achieving (0.0024414, 0.091936) respectively, followed by 2DPCA and PCA and all three techniques reached the highest accuracy rate of 100% for facial image recognition on real data. Paper type: Research paper

Research on face image recognition in smart city based on improved convolutional neural network

Research on face image recognition in smart city based on improved convolutional neural network

Feature decomposition-based gaze estimation with auxiliary head pose regression

Recognition and understanding of facial images or eye images are critical for eye tracking. Recent studies have shown that the simultaneous use of facial and eye images can effectively lower gaze errors. However, these methods typically consider facial and eye images as two unrelated inputs, without taking into account their distinct representational abilities at the feature level. Additionally, implicitly learned head pose from highly coupled facial features would make the trained model less interpretable and prone to the gaze-head overfitting problem. To address these issues, we propose a method that aims to enhance task-relevant features while suppressing other noises by leveraging feature decomposition. We disentangle eye-related features from the facial image via a projection module and further make them distinctive with an attention-based head pose regression task, which could enhance the representation of gaze-related features and make the model less susceptible to task-irrelevant features. After that, the mutually separated eye features and head pose are recombined to achieve more accurate gaze estimation. Experimental results demonstrate that our method achieves state-of-the-art performance, with an estimation error of 3.90° on the MPIIGaze dataset and 5.15° error on the EyeDiap dataset, respectively.

Sustainable Parking Innovations: Enhancing Security with IoT and Facial Image Recognition

Sustainable Parking Innovations: Enhancing Security with IoT and Facial Image Recognition

CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance

In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods.

An Adaptive Approach of Face Recognition for Low Intensity Images Using Texture Features

Abstract: Biometric recognition is the major field in the process of digitalization due to security. Face is an important biometric trait that can be used for security and authorization process. In this paper a novel approach has been proposed for face recognition process. Face recognition has major drawback of accuracy under low quality images that has improved in proposed approach. In this paper an approach of texture feature extraction has been hybrid with Dual cross pattern approach so that optimum set of feature can be computed. Experimental results have been extracted under variation in resolution. Proposed approach outperform to existing approaches.

INTRUDER DETECTION SYSTEM USING MACHINE LEARNING

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software. Keywords: Digital Image Processing, Face Detection, Face Recognition, Motion Detection

De-occlusion and recognition of frontal face images: a comparative study of multiple imputation methods

AbstractIncreasingly, automatic face recognition algorithms have become necessary with the development and extensive use of face recognition technology, particularly in the era of machine learning and artificial intelligence. However, the presence of unconstrained environmental conditions degrades the quality of acquired face images and may deteriorate the performance of many classical face recognition algorithms. Due to this backdrop, many researchers have given considerable attention to image restoration and enhancement mechanisms, but with minimal focus on occlusion-related and multiple-constrained problems. Although occlusion robust face recognition modules, via sparse representation have been explored, they require a large number of features to achieve correct computations and to maximize robustness to occlusions. Therefore, such an approach may become deficient in the presence of random occlusions of relatively moderate magnitude. This study assesses the robustness of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transformation for preprocessing and city block distance for classification (DWT-PCA/SVD-L1) face recognition module to image degradations due to random occlusions of varying magnitudes (10% and 20%) in test images acquired with varying expressions. Numerical evaluation of the performance of the DWT-PCA/SVD-L1 face recognition module showed that the use of the de-occluded faces for recognition enhanced significantly the performance of the study recognition module at each level (10% and 20%) of occlusion. The algorithm attained the highest recognition rate of 85.94% and 78.65% at 10% and 20% occlusions respectively, when the MICE de-occluded face images were used for recognition. With the exception of Entropy where MICE de-occluded face images attained the highest average value, the MICE and RegEM result in images of similar quality as measured by their Absolute mean brightness error (AMBE) and peak signal to noise ratio (PSNR). The study therefore recommends MICE as a suitable imputation mechanism for de-occlusion of face images acquired under varying expressions.

Пространственно-ориентационные параметры узнавания изображений лиц как предикторы симптомов печеночной недостаточности у подростков

Functional assessment of higher mental functions in case of intoxication or during treatment will make it possible to identify predictors of the symptoms of hepatic encephalopathy associated with portal hypertension. The study was aimed to determine the diagnostic predictors of the emergence of the earliest symptoms of hepatic encephalopathy in adolescents with portal hypertension. The study involved 60 adolescents aged 13–17 years: 28 males, 32 females. The experimental group included 30 adolescents with the diagnosis К76.6 Portal hypertension, unspecified form. The control group included 30 adolescents with normal somatic status, who had no mental disorders, traumatic brain injuries or severe infectious diseases of the brain (based on the records of the annual check-up). The studied groups were matched by sex, age, and social status. The proprietary method, Tobii EyeX hardware and software system (GazeControl software), and Cambridge Face Memory Test for Children (CFMT-C) were used. It has been found that recognition of single face images, multiple face images, and multiple face images camouflaged with noise by adolescents with portal hypertension is associated with the greater efforts (manifested in the increased number and duration of gaze fixations), than recognition of the above by adolescents with normal somatic status. The accuracy of recognition of single face images, multiple face images, and multiple face images camouflaged with noise shown by adolescents with portal hypertension experiencing the toxic effects associated with manifestations of hepatic encephalopathy is significantly reduced compared to that shown by adolescents with normal somatic status. The results obtained can be considered as the diagnostic predictors allowing one to trace the changes in the hepatic encephalopathy severity at various stages of treatment (including after surgical intervention).

Recovery-Based Occluded Face Recognition by Identity-Guided Inpainting.

Occlusion in facial photos poses a significant challenge for machine detection and recognition. Consequently, occluded face recognition for camera-captured images has emerged as a prominent and widely discussed topic in computer vision. The present standard face recognition methods have achieved remarkable performance in unoccluded face recognition but performed poorly when directly applied to occluded face datasets. The main reason lies in the absence of identity cues caused by occlusions. Therefore, a direct idea of recovering the occluded areas through an inpainting model has been proposed. However, existing inpainting models based on an encoder-decoder structure are limited in preserving inherent identity information. To solve the problem, we propose ID-Inpainter, an identity-guided face inpainting model, which preserves the identity information to the greatest extent through a more accurate identity sampling strategy and a GAN-like fusing network. We conduct recognition experiments on the occluded face photographs from the LFW, CFP-FP, and AgeDB-30 datasets, and the results indicate that our method achieves state-of-the-art performance in identity-preserving inpainting, and dramatically improves the accuracy of normal recognizers in occluded face recognition.

TIFd-FR: Trends, Issues and Future directions of feature extraction in Face Recognition

Today, face recognition is the most prevalent and effective mechanism among various biometric technologies as it is non-invasive method. It helps in identifying or verifying the identity of a person by utilizing its face. But face recognition (FR) can be prone to high error rate. Therefore, efficient feature extraction methods are required for extracting robust facial features to develop efficient FR system. A FR system comprises of mainly three phases, that is, face detection and orientation, extracting facial features and classification of features. The most vital part of an efficient recognition system is extraction of robust features. Hence, extracting facial features is active research area of image processing. Although algorithms have been developed for extracting features, efficient and robust feature extraction still offers great challenge to researchers. Hence, a thorough analysis of feature extraction techniques presented in this work will enable the researchers to select the best suited technique for developing efficient system for recognition of face images. The performance of different feature extraction methods varies under variations in illumination, occlusion and pose etc. It is observed that deep-learning based feature extraction methods outperform wavelet-based methods. After rigorous analysis of various state-of-art techniques it is found that highest accuracy rate of 99.5% is achieved on AR database using wavelet based feature extraction whereas 99.78% is attained using convolutional neural network accuracy and various results achieved till now are stated.

FaceBreach Alert: Multi-Modal Intrusion Detection emphasizing Face Recognition

Abstract: The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the muchstudied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software.

Sistem Buka-Tutup Sebuah Pintu Rumah Menggunakan Face Recognition Berbasis Mikrokontroler

Internet of Things technology is now widely used in all aspects of life including the application of smart homes. An automation technology is used to facilitate access at home, as well as access to the entrance of the house that uses facial image recognition (face detection). Human facial image recognition is one of the important technologies that continues to develop in the field of computer vision with its application in biometric recognition systems. One of them is by using the facial image recognition method, the Haar Cascade Classifier Algorithm. Therefore, a house door open-close system with facial recognition is designed using the ESP32-Cam module as a camera sensor. Face images were taken using the ESP32-Cam camera and processed using the ESP32 Microcontroller through the Arduino IDE program. Recognition results will be forwarded from the microcontroller to the PIR sensor, DC motor, buzzer, LED and website. The test result of this system is that if the detected face ID matches the data base, the PIR sensor will be active to detect the movement of objects in front of the entrance. When motion is detected, the buzzer as an alarm will be ON and the door will open and close automatically. If Face ID is not detected then the door will not open.

Face Image Recognition Algorithm Based on Label Complementation

<p>In face image recognition, labels play a fairly important role in recognition and classification, and rich and perfect labels can greatly improve the accuracy rate. However, it is almost impossible for the labels in the image to be recognized to describe the image completely and accurately. At the same time, the data obtained when feature extraction is performed on an image inevitably extracts a large amount of redundant and useless information at the same time, which affects the generalization performance of the model. Accordingly, we propose a face image recognition algorithm based on label completion in multi label learning. First, the SVD algorithm is used to remove redundant and useless information from the features of the original data by dimensionality reduction operation to obtain simplified sample attribute information, and the label completion algorithm is used to supplement the labels of the images using the extracted feature information. Finally the obtained label data as complete as possible is put into the extreme learning machine to construct the face recognition model and give the prediction results of the images. Experiments on the ORL dataset demonstrate that the algorithm can achieve good recognition results.</p> <p> </p>

Gravitational Deep Convoluted Stacked Kernel Extreme Learning Based Classification for Face Recognition

In recent times, researchers have designed several deep learning (DL) algorithms and specifically face recognition (FR) made an extensive crossover. Deep Face Recognition systems took advantage of the hierarchical framework of the DL algorithms to learn discriminative face characterization. However, when handling severe occlusions in a face, the execution of present-day methods reduces appreciably. Several prevailing works regard that, when face recognition is taken into consideration, affinity materializes to be a pivotal recognition feature. However, the rate of affinity changes when the face image for recognition is found to be illuminated, and occluded, with changes in the age of the subject. Motivated by these issues, in this work a novel method called Gravitational Deep Convoluted Stacked Kernel Extreme Learning-based (GDC-SKEL) classification for face recognition is proposed for human face recognition problems in frontal views with varying age, illumination, and occlusion. First, with the face images provided as input, Gravitational Center Loss-based Face Alignment model is proposed to minimize the intra-class difference, which can overcome the influence of occlusion in face images. Second, Deep Convoluted Tikhonov Regularization-based Facial Region Feature extraction is applied to the occlusion-removed face images. Here, by employing the Convoluted Tikhonov Regularization function, salient features are said to be extracted with an age-invariant representation. Finally, Stacked Kernel Extreme Learning-based Classification is designed. The extracted features are given to the Stacked Kernel Extreme Learning-based Classification and to identify testing samples Stacked Kernel is utilized. The performance of GDC-SKEL is evaluated on Cross-Age Celebrity Dataset. Experimental results are compared with other state-of-the-art classifiers in terms of face recognition accuracy, face recognition time, PSNR, and False Positive Rate which shows the effectiveness of the proposed GDC-SKEL classifier.

Face and number plate recognition for car anti-theft

Smart parking systems along with that is continuous development of new technologies, are widely applied to improve our lives. It can also add new technologies with advanced functions making it a multi-functional management system. Thanks to the anti-theft technologies that are installed, modern cars are significantly more difficult to steal than they once were. However, electrical systems can still experience issues, though and malfunction at some point. This paper suggests using video image recognition technology, at car parks and parking lots as an anti-theft solution, alerting the presence of the non-owner of vehicle in the driver’s seat. The system automatically predicts whether the driver is valid with the registration number plate. The image of the car is captured by the camera at the entrance gates of the parking lot. The proposed algorithm includes face recognition in images, building a deep learning convolutional network that classifies faces (subscriber’s images); using Cascade trainer to train number plate object recognition, vehicle number recognition through character recognition technique. The system can recognize reality through a personal computer connected to the camera at the scene or through photos and video files. Result, the model can face recognition and match to license plate in at a moment.

Face Image Segmentation Using Boosted Grey Wolf Optimizer.

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur's entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur's entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation.

Retracted: A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism.

[This retracts the article DOI: 10.1155/2021/3688881.].

An adaptive gravitational search optimization (AGSO)‐based convolutional neural network—long short‐term memory (CNN‐LSTM) approach for face recognition and classification

SummaryFace recognition and classification have gained increasing attraction in the recent decades due to their widespread adoption in real time application systems. Most of the conventional research efforts focused on developing face recognition frameworks using enhanced optimization‐based classification methods, they are hampered by issues such as computational complexity, increased overhead, limited capacity to handle large datasets, and lengthy processing time. The novel contribution of this paper is to develop a highly competent and precise face recognition methodology through an innovative mechanism. In this framework, the initial step involves face detection from input images using an analytical face parts detection methodology. Subsequently, the tutor face filtering (TFF) technique is applied to preprocess the face image, enhancing its quality and filtering out noise content. Following this preprocessing step, features are extracted from the processed image using the direction‐based pattern extraction (DBPE) model. To improve classifier accuracy, a novel adaptive gravitational search optimization (AGSO) technique is employed to select the optimal features during model training. Finally, an integrated deep learning model, referred to as convolutional neural network—long short‐term memory (LSTM), is utilized for accurate face image recognition based on the selected optimal features. To assess and compare the system's performance, various metrics are employed in the results analysis to demonstrate the superiority of the proposed approach.

Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images

Abstract: Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications.
 Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results.
 Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore.
 Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested.
 Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.