Face Recognition Algorithm Research Articles

Algorithms used for facial emotion recognition: a systematic review of the literature

INTRODUCTION: We currently live in a society that is constantly changing and technology has developed algorithms that allow facial emotion recognition, because facial expression transmits people's mood, feelings and state of soul. However, it is required that future research can improve the quality of emotion detection by improving the quality of the data set and the model used, for this reason, it is necessary to investigate other machine learning algorithms in the recognition of facial emotions, as they exist. identification deficiencies that limit the discrimination of extracted structural features.OBJECTIVE: The purpose of the article was to analyze the most used algorithms for facial emotion recognition, through a systematic literature review, according to the PRISMA method.METHOD: A search for information was carried out in articles published in open access such as: Scopus, Web of Science (WOS) and Association for Computing Machiner (ACM) in the period 2022 and 2023, totaling 38 selected articles.RESULTS: The results obtained indicate that the algorithms most used by the authors are SVM and SoftMax with a total of 17.65% each.CONCLUSION: It is concluded that the SVM and SoftMax algorithms are the most predominant, playing a crucial role in achieving optimal levels of precision in the training of the models. These algorithms, with their robustness and ability to deal with complex data, have proven to be fundamental pillars in the field of facial emotion recognition.

Recognizing facial emotion leveraging convolutional neural network

Facial expression recognition technology is a technology based on the combination of artificial intelligence technology and biology, and it is interdisciplinary research. The appearance of this technology shows the diversity of the development direction and application fields of computer technology, but it also means that the development of facial expression recognition technology needs not only the support of computer technology but also the exploration and progress of biology. At present, to recognize facial expressions a system usually contains three stages, namely, face detection stage, feature extraction stage, and face expression recognition stage. The facial expression recognition algorithm is transformed into a classification problem, and a convolutional neural network (CNN) is adopted, where features of the facial expressions could be automatically extracted. It takes as input a preprocessed image and can directly produce encoded features and predictions, leveraging the end-to-end strategy. It could discard the complicated intermediate modeling process of traditional machine learning. In this work a CNN is implemented for the recognition of facial expression on Fer2013 dataset. Moreover, the effectiveness of different numbers of CNN layers are testified on this problem. It could be concluded that with deeper architectures, the CNN tends to perform better.

A Real Time Employee Attendance Monitoring System using ANN

Face recognition refers to the technology that examines and contrasts a person's face characteristics to recognise or verify their identity. Recently, this technology has drawn a lot of attention due to the potential uses it may have in security, marketing, and law enforcement. Face recognition involves studying a picture or video of a person's face to identify features like the space between their eyes, the contour of their nose, and the curve of their mouth. The person's identity is then established or verified by comparing these characteristics to a database of previously saved pictures. A series of techniques called facial recognition algorithms are used to identify and authenticate persons based on the features of their faces. These algorithms compare a person's facial attributes to those in a database of recognised faces by looking at things like the shape of their face, the distance between their eyes, and other distinctive facial features. There are many different types of face recognition algorithms, including geometric-based algorithms, appearance-based algorithms, and hybrid algorithms that combine both approaches. Geometric-based algorithms employ the geometry of face traits to identify and validate people, while appearance-based algorithms use image processing techniques to compare the patterns and textures of facial features. Recent advances in deep learning have significantly improved the accuracy of facial recognition algorithms. Artificial Neural Network (ANN) has shown to be highly effective and have been used in a range of applications, including mobile devices, security, and surveillance. Face recognition algorithms provide advantages, but there are also moral dilemmas with regard to its application, such as potential biases and privacy difficulties. As technology advances, it is imperative to address these problems and ensure that face recognition algorithms are used ethically and responsibly.

Fairness in face presentation attack detection

Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios. Despite the growing number of fairness studies in FR, the fairness of face presentation attack detection (PAD) has been overlooked, mainly due to the lack of appropriately annotated data. To avoid and mitigate the potential negative impact of such behavior, it is essential to assess the fairness in face PAD and develop fair PAD models. To enable fairness analysis in face PAD, we present a Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels. Then, we comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions. Additionally, we propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance. The experimental results pointed out that female and faces with occluding features (e.g. eyeglasses, beard, etc.) are relatively less protected than male and non-occlusion groups by all PAD solutions. To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues. The extensive experimental results indicate that FairSWAP leads to better-performing and fairer face PADs in 10 out of 12 investigated cases.

A dynamic fusion of features from deep learning and the HOG-TOP algorithm for facial expression recognition

A dynamic fusion of features from deep learning and the HOG-TOP algorithm for facial expression recognition

Review on Facial Changes Across Age Progression of the same Individual and its Application in Forensics

Introduction: It is diffi cult to describe or analyze face aging since it is a complex process driven by both intrinsic and extrinsic factors. Face aging diff erently infl uencesthe facial components of an individual such as the nose, mouth, and eyes. Objective: In this paper, we have discussed reviewing facial features, that change with the progression of age and techniques available to recognize these changes. Method: The majority of the work on face recognition has been carried out on adults and less of the work is reported on facial aging. Researchers have tried to develop algorithms for facial recognition, verifi cation, and identifi cation system which will have low false positive rates and high true positive rates due to the age infl uencing factor. Conclusion: It will help the scientifi c community for better analysis and recognition.

Feature Selection From MagFace Face Recognition Model With Optimization Algorithms

In recent years, many studies have been carried out in the field of artificial intelligence in the literature with the development of equipment. Face recognition algorithms have an important place among these developments. Among the face recognition algorithms, the most successful ones are usually deep learning approaches. Models such as SphereFace, CosFace, ArcFace, and MagFace are important deep learning models in the literature. Despite their success, deep learning models are often computationally costly. Therefore, advanced methods are needed to reduce the computational load for these models. One of the most valid methods for this is to choose the most valuable one among embedding features for face recognition. Thus, cost can be reduced, and accuracy values can be increased even more. In this study, the most valuable of the 512 embedded features in the MagFace model was tried to be obtained by using PSO, GA, SCA, and DE optimization algorithms. As a result, accuracy values of 99.83%, 98.57%, and 98.65% were reached for 193, 252, and 280 features selected in the LFW, CFP, and AGEDB datasets, respectively.

Real-Time Face Recognition System with Enhanced Security Features using Deep Learning


 
 
 
 Abstract: Identification of people and mask detection has long been a captivating topic, in terms of research and business. This topic has received increasing attention in recent phases due to the speedy advancement of Artificial Intelligence (AI). Nowadays, a lot of applications, including phone unlocking systems, criminal identification systems, and even home security systems, use face recognition as a common technique. Due to the fact that this method only requires a facial image instead of other dependencies like a key or card, it is more secure. Face detection and face identification are often the first two elements of a human recognition system. Even during COVID-19, it is considered the best way to stop the spread of the COVID-19 virus is by wearing a face mask. The risk of contracting the virus can be reduced by almost 70% only by wearing a face mask. In order to promote community health. This Study aims to produce a highly precise and real-time method that can effectively recognize people and identify non-mask faces in public. When a person stands in front of the device, this application detects the human face automatically using detection, extraction, and recognition algorithms. The proposed work applies the Viola-Jones algorithm for face recognition and the YOLOv5 algorithm for mask detection and classification. When the proposed work is tested, this shows higher accuracy in mask detection which is 92.8%.
 
 
 

Face Recognition and Eye Blink Attendance Management System

This research explores the implementation and advantages of utilizing facial recognition technology for automated student attendance management in educational environments. The study outlines a comprehensive system that employs a high-resolution digital camera to detect and recognize students' faces, comparing them with stored images in a database. Successful matches result in real-time attendance logging, enhancing record-keeping and future analysis. The system also accommodates the inclusion of new images when no match is found. By embracing deep learning advancements, the technology offers an efficient alternative to conventional roll-call procedures, enabling automatic head counting in classrooms. The system integrates various face recognition algorithms, leveraging attributes such as shape, color, Local Binary Pattern (LBP), wavelet transforms, and auto-correlation. While challenges like varying lighting conditions, facial expressions, and face orientation are acknowledged, this research underscores the potential of technology to revolutionize attendance management and student engagement in education. Key Words: facial recognition technology, student attendance, automated system, deep learning, real-time logging, attendance management, classroom headcount, face recognition algorithms, record-keeping.

Next-generation attendance tracking: Automated face detection for lecture attendance

Face detection technology has garnered much interest due to its potential uses in industries like face identification and video surveillance systems. In addition to being a component of the automatic face recognition framework, real-time face detection is now becoming its study area. There are numerous methods to handle the problem of face detection. Traditional methods of manual attendance tracking in lecture halls are time-consuming, prone to errors, and lack efficiency. Next-generation attendance tracking systems are incorporating automated face detection technology to address these challenges. This paper explores the implementation of automated face detection for lecture attendance, leveraging computer vision and facial recognition algorithms. Initially, students’ data were gathered and preprocessed using a Median filter (MF). Then the preprocessed data features were extracted using a histogram of gradients (HOG). And finally, extracted features were classified using a Backpropagation Bayesian neural network (BBNN). To prove the efficiency of the suggested method, that is compared and contrasted with specific conventional methods. Experimental outcomes show that the recommended method performs superior when contrasted to traditional methods.

Towards face anti-spoofing

ABSTRACT Face anti-spoofing is an important part of the face recognition algorithm that aims to prevent face presentation attacks. To facilitate face anti-spoofing research, this paper introduces a novel method in which a simple model provides enhanced performance on the RGB modality images of the CASIA-SURF dataset. Initially, a simple model based on ResNet-50 architecture is proposed that uses three residual layers of ResNet-50 along with a single neuron at the end for binary classification. Meanwhile, to judge the baseline performance, the proposed Resnet-50-based model is trained on the RGB and Depth images, separately. Later, to increase the performance of the proposed ResNet-50-based model on the RGB modality images, a novel Learning Using Privileged Information (LUPI) algorithm is applied. The LUPI contains Depth images and helps to train the inferior model with the help of a pre-trained superior model. Detailed simulations on the CASIA-SURF dataset reveal that an intelligent combination of the ResNet50 based model with the LUPI paradigm works exceptionally well and significantly improves the performance on the RGB modality of the images. The proposed model is less complex and can be deployed in resource-constrained environments.

Face recognition technology based on low-rank joint sparse representation algorithm

With the improvement of computer computing power and the development of artificial intelligence technology, face recognition technology has made a major breakthrough, and has been popularized and applied in all areas of life. However, different face structure and pose will affect the accuracy of face recognition. To overcome the problem, a low rank joint sparse representation algorithm for face recognition is proposed. The low rank features of images are extracted by structure independent and pairwise rank decomposition methods. The extracted low rank features of the first level image and the low rank features of the second level image are sparsely represented. Finally, the residual rate model is used to classify the images, and the final result of face recognition is obtained. The experimental results show that the proposed SRP algorithm has a recognition accuracy of more than 92% in two different face recognition tests. In the mixed multi face pose test, PRS algorithm performs best in the recognition of 1, 2, 3, 4, and 5 multi face pose types, with recognition rates of 95%, 94%, 93%, 91%, and 90% respectively. The algorithm also has excellent recognition performance and robustness in identifying harsh environments such as fuzzy environments. The research content focuses on complex face recognition scenes, innovatively uses low rank to complete the extraction of face feature data, and combines sparse selection of classification features to improve the overall effect of face recognition. It has important reference value for improving the overall security and recognition rate of face recognition.

Automatic Gate Opening System with Face Recognition

Despite the limitations of current technology, facial recognition from still or moving sources is developing into a useful tool for law enforcement, security, and counterterrorism applications. Currently, facial recognition shows promise and has been used in a small number of applications, The effectiveness of a facial recognition algorithm is examined in this assessment, specifically with regards to factors such as age, gender, ethnicity, facial characteristics, and lighting levels. Face recognition is being used more frequently as a non-intrusive identity verification technique. In this paper, we propose for the first time a strongly privacy-enhanced face recognition system that effectively uses secure multiparty computation techniques to conceal both the biometrics and the result from the server performing the matching operation. These issues can be easily solved by Explainable AI (XAI), and the solutions are clear to the end users. The proposed system is entirely implemented on a Raspberry Pi, enabling a full embedded application. With the aid of open-source libraries for training, defining, and running machine learning models like Tensorflow.js, Keras, and OpenCV, the application was created using Python and HTML on PyCharm/Visual Studio Code. An entire application can be run on a microcontroller, like the Raspberry Pi, which enables plug-and-play operation of the system at any time. The face detection subsystem, which is based on an improved PCA algorithm, can identify faces in moving vehicles and communicate with the vehicle's owner via GSM. The car's location is sent via GPS. This is the most reliable car security system because we can still locate the car even when it is lost and we can see the thief's face. The owner of the car is free to give the password to new drivers as well. This makes the car security system safer and more comfortable.

Enhancing eID card mobile-based authentication through 3D facial reconstruction

In recent years the number of credentials and digital identities needed to access online services is exploding, increasing the risk of fraud and unauthorized access to personal data.Biometric verification techniques can help to build more secure authentication systems; in particular face recognition algorithms have reached near perfect accuracy under ideal conditions. In Europe 60% of population owns an electronic identification (eID) document which contains biometric data, and almost all member states provide tools to verify the identity of a user who holds an eID card; however, the adoption of biometric authentication schemes is still limited. Reasons for this low adoption are privacy concerns, about the storage or transmission of personal images, and the degradation of performance of facial recognition algorithms with 2D images acquired "in the wild" (real world condition with inconsistency in lighting and positioning, unclear or occluded facial features, noise from the background). In this paper a new authentication scheme is proposed, exploiting images acquired "in the wild" and 3D facial reconstruction techniques which can help in improving the quality of the probed images, as changes in lighting or position have limited impact on facial shape. Several experiments have been conducted in order to investigate the performance of the proposed method, using the Bosphorus and the NIST Face Recognition Grand Challenge (FRGC) datasets. In particular, one of the most accurate ML face recognition algorithms has been selected to compare the performance of 2D snapshot data, obtained from 3D back-projection, against 2D uncontrolled images acquired "in the wild". We show that using 3D facial reconstructions significantly improve face recognition performance, allowing for a more secure and robust authentication system.

A Review on Identity Management Using Face Detection Techniques

Face recognition, which has applications in iris and fingerprint recognition in addition to face recognition itself, is a crucial component of computer vision and object recognition. In everyday life, face detection is critical in security and forensic investigations. Face identification, whether in fixed photos or video sequences, poses considerable hurdles due to elements such as facial expressions, position fluctuations, occlusion, aging, and resolution. The authors propose the notion of face synthesis to overcome these issues and improve accuracy. They want to enhance recognition rates when working with different face databases by using face synthesis techniques. The study provides an in-depth examination of various facial recognition algorithms, highlighting the problems involved and emphasizing the need to improve efficiency and recognition rates. for large face databases. To show the efficacy of the suggested strategies, the authors compare rates of accuracy and recognition

An adaptive system for predicting student attentiveness in online classrooms

The effectiveness of teaching methods is determined by whether a student is attentive in a lecture or not. In face-to-face classroom teaching, a teacher is able to judge whether students are understanding the subject, based on their facial expressions. However, since the uprise of COVID-19 pandemic, virtual classrooms have found a holding in the field of education and detecting attentiveness of students is a challenge in the same. This paper proposes a student attentiveness model that would detect and monitor a student’s eye state to determine their level of attentiveness and provide a real-time feedback mechanism to the teacher. The proposed model employs a histogram of oriented gradient (HOG) method in conjunction with support vector machine (SVM) algorithm for face recognition. It then computes an adaptive eye aspect ratio (AEAR) for each individual student to determine their level of attentiveness. The model is tested on a real-time dataset and validated using classifiers (SVM, decision tree, and random forest). The results of the classifiers verify that the model produces an accuracy of more than 92%.

Study on Facial Recognition Method Based on YOLOv5

This study focuses on the facial recognition algorithm. To address limitations of facial recognition, this paper proposes a deep learning method called YOLOv5-Attention for facial feature extraction, recognition. The algorithm in the article is designed based on YOLOv5 and incorporates an attention mechanism module to enhance the expression ability of the model’s eyebrow features and improve its overall performance. The experimental results demonstrate that the algorithm proposed in this paper achieves a harmonic mean of 94% for eyebrow recognition accuracy. This highlights the effectiveness and reliability of the algorithm, which can be applied to eyebrow recognition and dramatic character facial recognition, as well as the research of general facial recognition.

Analysis and Recognition of Voluntary Facial Expression Mimicry Based on Depressed Patients.

Many clinical studies have shown that facial expression recognition and cognitive function are impaired in depressed patients. Different from spontaneous facial expression mimicry (SFEM), 164 subjects (82 in a case group and 82 in a control group) participated in our voluntary facial expression mimicry (VFEM) experiment using expressions of neutrality, anger, disgust, fear, happiness, sadness and surprise. Our research is as follows. First, we collected a large amount of subject data for VFEM. Second, we extracted the geometric features of subject facial expression images for VFEM and used Spearman correlation analysis, a random forest, and logistic regression-based recursive feature elimination (LR-RFE) to perform feature selection. The features selected revealed the difference between the case group and the control group. Third, we combined geometric features with the original images and improved the advanced deep learning facial expression recognition (FER) algorithms in different systems. We propose the E-ViT and E-ResNet based on VFEM. The accuracies and F1 scores were higher than those of the baseline models, respectively. Our research proved that it is effective to use feature selection to screen geometric features and combine them with a deep learning model for depression facial expression recognition.

Biometric Enabled Remote Voting System for Senior Citizens

- In developing countries, democracy plays a crucial role, but conventional voting systems often pose challenges due to resource and manpower consumption, potential illegal activities, and lack of transparency. To address these issues, a new confidential E-Voting system is proposed, incorporating facial recognition technology for secure and efficient voting. This system aims to provide an inclusive and accessible solution, especially for senior citizens who face difficulties with traditional voting methods due to mobility constraints. The online voting platform offers a user-friendly web interface and secure authentication using facial recognition algorithms. Seniors can conveniently vote remotely through their preferred devices, ensuring precise identification and preventing fraud. The system's implementation involved modern facial recognition technology, emphasizing usability and security. Testing and validation confirmed its effectiveness, reduced unauthorized access risks, and received positive user feedback regarding simplicity and accessibility. By incorporating facial recognition, this online voting system offers increased convenience, accessibility, and cost-efficiency, promoting inclusivity for seniors regardless of their location or mobility. The study contributes to the field of accessible voting technology and paves the way for future improvements in online voting systems catering to diverse demographic groups. Key Words: Online voting, Senior citizens, facial recognition, candidates, and voters

Improvement Airport Security System with Face Recognition Using Neural Network Based on the Arduino Uno Microcontroller

The aim of this study is to implement an algorithm for face recognition, based on the Arduino uno microcontroller. This paper presents it as an airport security system to detect passenger’s face and compare the result with the database of unwanted people. The system combines laser trigger circuit for image capturing and artificial neural network for image recognition. The laser trigger circuit has many benefits such as avoiding camera shakes or taking a picture without a timer. The captured image will be analyzed and processed in MATLAB using an artificial neural network to recognize the passenger’s face from the real captured images after the training phase. Many experiments have been conducted on our face databases with various numbers of iterations. The recognitions’ accuracy and efficiency of the proposed model are 93.33% and 2.67 respectively with 0.696530 seconds as execution time. The result shows the robustness of the developed model in terms of mean squared error, execution time, recognitions’ efficiency and accuracy. The smallest obtained mean squared error is 9.9991e-04 for the training data set and 0.1764 for the testing data set when they are recorded for a modified neural network which makes the developed system more reliable. Finally, the artificial neural network demonstrates the ability to detect the unseen relationship between features belong to the same face.