Face Recognition Method Research Articles

A Review of Facial Expression Recognition Based on Convolutional Neural Networks

Abstract. The intelligent processing of human facial expressions has gained popularity due to the growth of big data and the expanding knowledge of AI and machine learning. In this paper, the author reviews the current mainstream methods for face recognition using convolutional neural network (CNN) models in deep learning, providing insights and future directions. By collecting and analyzing research findings, the characteristics, strengths and weaknesses of each model are discussed. The results indicate that CNN-based face recognition systems typically involve several steps: face detection, face alignment, face representation, face matching, and post-processing. By examining expression recognition with models like LeNet-5, AlexNet, VGGNet, and GoogleNet, the author concludes that CNNs possess features such as being data-driven, capable of feature learning, multi-level structured, adaptable to different domains, capable of real-time processing, and supporting multi-task learning. This technology has made significant strides and offers considerable potential for further development.

A Comprehensive Analysis of Face Recognition Based on Advanced Deep Learning Technology

Abstract. Facial recognition technology is becoming increasingly important in many fields due to its non-invasiveness and rapid improvements in accuracy. This study provides a comprehensive review of face recognition methods, covering both traditional and modern approaches. The key models such as feature face, Fischer face, Visual Geometry Group face (VGG-Face), FaceNet and ArcFace are analyzed, and the complete process from data preprocessing to feature extraction and model training is described in detail. By using the Wild Labeled Face Dataset (LFW) and the Network Face Dataset of the Institute of Automation of the Chinese Academy of Sciences (CASIA-WebFace) for experiments, the research found that although the deep learning model has high accuracy, it also faces challenges such as data dependence and large computing requirements. At the same time, the research highlights the issues of fairness and bias in these models, providing directions for future research and contributing to the sustainable and ethical development of face recognition technology.

An improved student’s facial emotions recognition method using transfer learning

An improved student’s facial emotions recognition method using transfer learning

New Extracted Features to Recognize Faces Effected by Occlusions and Common Variations

Face recognition from non-identical face photos is a prominent area of research in pattern recognition and computer vision. Existing face recognition systems struggle with diverse changes like lighting conditions, expressions, and facial occlusions. This paper proposes a new Face Recognition (FR) approach that combines the Elastic Bunch Graph Matching (EBGM) approach with the greedy algorithm to automatically identify face landmarks. The proposed approach independently selects each optimal landmark of face image from different corresponding face images where the corresponding landmark of corresponding face image which achieves the best similarity is used rather than using one or at most two corresponding face images and computing the average between both. The locations of corresponding landmarks can be displaced to achieve maximum similarity with optimal landmarks. This proposed approach demonstrates improved recognition performance compared to contemporary face recognition methods. It effectively handles changing ratios of face parts and can recognize faces even with increasing occlusion sizes.

Face Recognition with Siamese Networks

Abstract Face recognition method is a common problem in modern Internet life. In this paper, face recognition will be designed based on Siamese network. The traditional methods have some problems, such as being greatly affected by environmental factors, insufficient processing of individual differences, lack of robustness and adaptability. However, the existing Siamese network based method has better recognition effect. In this paper, a face recognition technology based on Siamese network will be proposed. In this method, the features of two face images are extracted by the same neural network, and then these feature vectors are input into a distance metric model to calculate the similarity or distance between them, which is used to compare the similarity between faces. This method reduces the training parameters of the model, improves the utilization of parameters, and the average accuracy can reach 98%. Contrasted with traditional face recognition techniques, it offers enhanced accuracy and resilience, delivering favourable outcomes in real-world applications.

Face recognition method based on fusion of improved MobileFaceNet and adaptive Gamma algorithm

MobileFaceNet face recognition algorithm is a relatively mainstream face recognition algorithm at present. Its advantages of small memory and fast running speed make it widely used in embedded devices. Due to the limited face image acquisition capability of embedded devices, the accuracy of face recognition is often reduced due to uneven illumination and poor exposure quality. In order to solve this problem, a face recognition algorithm based on the fusion of MobileFaceNet and adaptive Gamma algorithm is proposed. The application of the algorithm proposed in this paper in image preprocessing is as follows. Firstly, adaptive Gamma correction is used to improve the brightness of the face image. Then, the edge of the face image is enhanced by the Laplace operator. Finally, a linear weighted fusion was performed between the Gamma corrected image and the enhanced edge image to obtain the pre-processed face image. At the same time, we have improved the traditional MobileFaceNet network. The feature extraction network MobileFaceNet has been improved by adding a Stylebased Recall Module (SRM) attention mechanism to its bottom neck layer, utilizing the mean and standard deviation of input features to improve the ability to capture global information and enhance more important feature information. Finally, the proposed method was verified on the LFW and Agedb face test set. The experimental results show that the adaptive Gamma algorithm proposed in this paper and the improvement of MobileFaceNet can achieve a face recognition accuracy of 99.27 % on LFW dataset and 90.18 % on Agedb dataset while only increasing the model size by 0.4 M and the processing speed for each image is enhanced by 4 ms. which can effectively improve the accuracy of face recognition and better application prospects on embedded devices. The method presented in this article has certain practical significance.

Enhancing Public Safety: A Comprehensive Analysis of Crime Vision Strategies

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. HAAR feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique.

A Face Recognition Method for Sports Video Based on Feature Fusion and Residual Recurrent Neural Network

A Face Recognition Method for Sports Video Based on Feature Fusion and Residual Recurrent Neural Network

Face Recognition-Based Surveillance System in Mining Industry

Access control in mining construction areas is crucial for the operations of mining companies. This access control functions to secure and restrict unauthorized parties from mining activities. Violations of access rights in the mining industry result in significant losses for companies. This access control can also be utilized to record employee attendance, serving as input for the contract work system commonly applied in mining areas. Closed-circuit television (CCTV) is commonly used to monitor activities; however, the current use of CCTV still requires direct human observation, which may result in important events being overlooked. The functionality of these CCTVs can be enhanced to manage access rights and monitor employee attendance to support company operations through face recognition methods. In this study, a system design was carried out through a research approach to determine the technology to be used in the system. The development of a face recognition-based access control system was conducted based on system engineering methodology. This development includes system requirements analysis, the design of a face recognition-based access control system, implementation, and system performance evaluation. The resulting system was tested through simulation processes based on actual field conditions, and the test results showed that the system could recognize faces registered in the dataset and identify subjects not registered in the dataset with an accuracy of 60%, precision of 96%, recall of 58%, and an F-score of 72%. Additionally, the system was able to connect to a database to store face recognition results and then display them on an employee attendance monitoring dashboard. The delay between the face recognition system and actual time ranged from 2-4 seconds and was still tolerable.

A fast mask synthesis method for face recognition

Mask face recognition has recently gained increasing attention in the current context. Face mask occlusion seriously affects the performance of face recognition systems, because more than 75% of the face area remains unexposed and the mask directly causes an increase in intra-class differences and a decrease in inter-class separability in the feature space. To improve the performance of face recognition model against mask occlusion, we propose a fast and efficient method for mask generation in this paper, which can avoid the need for large-scale collection of real-world mask face training sets. This approach can be embedded in the training process of any mask face model as a module and is very flexible. Experiments on the MLFW, MFR2 and RMFD datasets show the effectiveness and flexibility of our approach that outperform the state-of-the-art methods.

PointSurFace: Discriminative point cloud surface feature extraction for 3D face recognition

Due to the geometric information in the 3D face data, the 3D face recognition methods exhibit better robustness against the physical attacks compared to the 2D recognition methods. In this paper, we propose PointSurFace, a 3D face point cloud recognition method integrated with a specially designed facial surface feature extraction (SurF) module. In PointSurFace, SurF comprehensively combines the coordinate positions, normal vectors, and angle relationships, etc., of each patch in the 3D face point cloud, to explore the explicit local geometric information of 3D faces and generate facial surface features. Subsequently, the surface features are fed into an encoder consisting of four set abstraction (SA) modules and three Inverted Residual MLP (InvResMLP) modules to extract more discriminative 3D facial features, where InvResMLP could suppress the overfitting and reduce information loss in the model training. In addition, PointSurFace utilizes channel fusion operations to integrate position information into the extracted 3D facial features, further enhancing the model performance. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing the previous methods by 2.02% and 3.19%. The ablation studies evaluate each module of PointSurFace, suggesting that the proposed surface feature extraction module significantly enhances the model performance.

A Novel OTP Based Pin Generation and Face Recognition Methods in Online Banking

A Novel OTP Based Pin Generation and Face Recognition Methods in Online Banking

The development history and future prospects of artificial intelligence in facial recognition technology

In recent decades, with the fast development of various algorithms and the rapid improvement of computer hardware performance, the development of facial recognition technology has been very rapid. This technology used to only appear in science fiction movies, but now it has been widely used in mobile payments, identity recognition, and other fields, and it also has very reliable performance. This article introduces the different facial recognition methods used in different periods of facial recognition technology from the 1960s and 1970s to the present day, to help people understand the development process of facial recognition technology. Among them, the focus is on early methods based on geometric features, grayscale information, and feature extraction, as well as recent applications based on deep learning and convolutional neural networks. In response to some challenges faced by current technology, some speculations on possible future development directions are proposed.

Open-Set Sheep Face Recognition in Multi-View Based on Li-SheepFaceNet

Deep learning-based sheep face recognition improves the efficiency and effectiveness of individual sheep recognition and provides technical support for the development of intelligent livestock farming. However, frequent changes within the flock and variations in facial features in different views significantly affect the practical application of sheep face recognition. In this study, we proposed the Li-SheepFaceNet, a method for open-set sheep face recognition in multi-view. Specifically, we employed the Seesaw block to construct a lightweight model called SheepFaceNet, which significantly improves both performance and efficiency. To enhance the convergence and performance of low-dimensional embedded feature learning, we used Li-ArcFace as the loss function. The Li-SheepFaceNet achieves an open-set recognition accuracy of 96.13% on a self-built dataset containing 3801 multi-view face images of 212 Ujumqin sheep, which surpasses other open-set sheep face recognition methods. To evaluate the robustness and generalization of our approach, we conducted performance testing on a publicly available dataset, achieving a recognition accuracy of 93.33%. Deploying Li-SheepFaceNet on an open-set sheep face recognition system enables the rapid and accurate identification of individual sheep, thereby accelerating the development of intelligent sheep farming.

Ethics-aware face recognition aided by synthetic face images

Current face recognition models trained on large-scale face datasets have achieved promising performance. However, using face images to train a face recognition model without consent would lead to severe privacy and ethical issues. Moreover, existing face recognition models also exhibit uneven performance on different races, thus perplexing vulnerable populations. To address the aforementioned two issues, this work investigates an ethics-aware face recognition method and examines whether we can leverage synthesized faces to achieve a high-accuracy racial balanced recognition model. In a nutshell, we introduce a race-controllable and identity-innumerable face synthesis approach to generate synthetic face images, and then employ the synthesized images to improve face recognition accuracy and mitigate recognition imbalance among different races despite the scarcity of consenting images (less than 100 individuals). More importantly, the synthetic data enable us to analyze the potential impacts of races on face recognition models quantitatively and facilitate the eradication of racial imbalance in face recognition. Extensive experiments demonstrate that employing our synthetic face data improves face recognition accuracy by a large margin while mitigating the recognition imbalance across different race groups.

Boosting cattle face recognition under uncontrolled scenes by embedding enhancement and optimization

Accurate individual cattle identification is crucial for modern precision cattle farming. However, practical applications encounter challenges due to factors such as shooting distance and angles, cattle movements, weather conditions, and cattle face posture. Existing models struggle with low recognition accuracy for low-recognizable images and poor robustness to facial pose variations, presenting urgent problems that need to be addressed. In this study, a novel cattle face recognition method was proposed, aiming to solve the aforementioned issues by analyzing and optimizing the embedding distribution of cattle faces. Firstly, MobileFaceNet was employed as the feature extraction network to sufficiently extract discriminative representations. Secondly, due to the tendency of low-recognizable samples to cluster in specific areas of the embedding space, an Embedding Enhancement Module (EEM) was proposed. This module drives embedding features away from ineffective embedding spaces, thereby enhancing the model's ability to extract identity features from less recognizable cattle faces. Finally, an Embedding Optimization Module (EOM) was proposed, which utilized a Sub-Center method to alleviate the model's learning difficulty during early training stages and achieved "pose-irrelevant" clusters by merging Sub-Centers, enhancing robustness to facial pose variations. Experimental results confirmed the effectiveness of the proposed method, with it outperforming the baseline by up to 2.12 % in accuracy, achieving a recognition accuracy of 98.69 %. Furthermore, the application of the proposed method in cattle farms further demonstrated the significant potential value of this work.

A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching.

As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support.

Aplikasi Presensi Pegawai dengan Metode Face Recognize dan Location-Based Service berbasis Android

Presence is a one of main thing for a State Civil Apparatus (ASN). However, in its implementation, the ASN attendance process currently still uses conventional methods and finger machines, which causes several problems, such as ineffectiveness, inefficiency, and vulnerability to fraud. This research is aimed to develop a presence application using Android-based face recognition and location-based service methods. This study type is research and development use the Software Development Life Cycle (SDLC) Spiral that focuses on paying attention to project risks. The liason or data collection stage used is a literature study by looking for relevant study, then direct observation in the field interviews with BKPSDM Bondowoso regarding presence issues, and a questionnaire to collect feedback from respondents as material for further application development, then the data collection results will be used to analyze needs, features and application specifications. The risk analysis stage aims to identify risks that may occur. The engineering stage of the application was built using the Flutter framework and RestfulAPI. The results of application testing state that the application can perform according to its functionality and needs. The study results is an Android application that aims to assist ASN in facilitating attendance via smartphones. According to a survey of 500 people, the application was positively received by Bondowoso County Government.

Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and Solutions

Abstract Recent advancements in face recognition (FR) technology in surveillance systems make it possible to monitor a person as they move around. FR gathers a lot of information depending on the quantity and data sources. The most severe privacy concern with FR technology is its use to identify people in real-time public monitoring applications or via an aggregation of datasets without their consent. Due to the importance of private data leakage in the FR environment, academia and business have given it a lot of attention, leading to the creation of several research initiatives meant to solve the corresponding challenges. As a result, this study aims to look at privacy-preserving face recognition (PPFR) methods. We propose a detailed and systematic study of the PPFR based on our suggested six-level framework. Along with all the levels, more emphasis is given to the processing of face images as it is more crucial for FR technology. We explore the privacy leakage issues and offer an up-to-date and thorough summary of current research trends in the FR system from six perspectives. We also encourage additional research initiatives in this promising area for further investigation.

Assessing inclusion and representativeness on digital platforms for health education: Evidence from YouTube

BackgroundStudies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube. MethodsLeveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video’s average daily view count. A video that generates a higher view count is considered to be more popular. ResultsThe voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78%, AUC = 76%), while the gender and race predictions use facial recognition (accuracy = 93%, AUC = 92% and accuracy = 82%, AUC = 80%, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non − white group have high view counts. ConclusionPresenters’ demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.