Face Recognition Approach Research Articles

Techniques for facial affective computing: A review

Facial affective computing has gained popularity and become a progressive research area, as it plays a key role in human-computer interaction. However, many researchers lack the right technique to carry out a reliable facial affective computing effectively. To address this issue, we presented a review of the state-of-the-art artificial intelligence techniques that are being used for facial affective computing. Three research questions were answered by studying and analysing related papers collected from some well-established scientific databases based on some exclusion and inclusion criteria. The result presented the common artificial intelligence approaches for face detection, face recognition and emotion detection. The paper finds out that the haar-cascade algorithm has outperformed all the algorithms that have been used for face detection, the Convolutional Neural Network (CNN) based algorithms have performed best in face recognition, and the neural network algorithm with multiple layers has the best performance in emotion detection. A limitation of this research is the access to some research papers, as some documents require a high subscription cost.
 Practice implication: The paper provides a comprehensive and unbiased analysis of existing literature, identifying knowledge gaps and future research direction and supports evidence-based decision-making. We considered articles and conference papers from well-established databases. The method presents a novel scope for facial affective computing and provides decision support for researchers when selecting plans for facial affective computing.

Sistem Absensi Cerdas Menggunakan Open CV Berbasis Pengenalan Wajah

Abstract
 
 The face is an important part of the human body uniquely identify a person. Using facial characteristics as biometrics, facial recognition systems can be implemented. That the most demanding task in any organization is marking attendance. In the traditional attendance system, students are summoned byteachers and their presence or absence marked accordingly. However, this traditional technique is time consuming and boring. In this project, Open CV is based on face recognition approach has been proposed. This model integrates a camera captures the input image, an algorithm for detecting the face of a input images, encoding and identifying faces, tagging presence in a spreadsheet and convert it to a PDF file. That the training database is created by training the system with faces from authorized students. The cropped image is then saved as a database with their respective labels. The features are extracted using the LBPH algorithm.
 
 Keywords: LBPH, , Attendance, Biometric, Face Recognition

Facial emotion recognition using geometrical features based deep learning techniques

In recent years, intelligent emotion recognition is active research in computer vision to understand the dynamic communication between machines and humans. As a result, automatic emotion recognition allows the machine to assess and acquire the human emotional state to predict the intents based on the facial expression. Researchers mainly focus on speech features and body motions; identifying affect from facial expressions remains a less explored topic. Hence, this paper proposes a novel approach for intelligent facial emotion recognition using optimal geometrical features from facial landmarks using VGG-19s (FCNN). Here, we utilize Haarcascade to detect the subject face and determine the distance and angle measurements. The entire process is to classify the facial expressions based on extracting relevant features with the normalized angle and distance measures. The experimental analysis shows high accuracy on the MUG dataset of 94.22% and 86.45% on GEMEP datasets, respectively.

A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

Facial recognition technology has been used in many fields such as security, biometric identification, robotics, video surveillance, health, and commerce due to its ease of implementation and minimal data processing time. However, this technology is influenced by the presence of variations such as pose, lighting, or occlusion. In this paper, we propose a new approach to improve the accuracy rate of face recognition in the presence of variation or occlusion, by combining feature extraction with a histogram of oriented gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the Canny contour detector techniques, as well as a convolutional neural network (CNN) architecture, tested with several combinations of the activation function used (Softmax and Segmoïd) and the optimization algorithm used during training (adam, Adamax, RMSprop, and stochastic gradient descent (SGD)). For this, a preprocessing was performed on two databases of our database of faces (ORL) and Sheffield faces used, then we perform a feature extraction operation with the mentioned techniques and then pass them to our used CNN architecture. The results of our simulations show a high performance of the SIFT+CNN combination, in the case of the presence of variations with an accuracy rate up to 100%.

Brightness Based Password Safeguard System with Face Recognition in ATM

the importance of security in the authentication process as well as the increase in threat level posed by such malware has attracted many researchers to the field. Many attacks are successful in accessing social network accounts since the current password-based authentication paradigms are not efficient and robust enough as well as vulnerable to automated attacks. The traditional two-factor authentication mechanisms are not applicable to online social networks because physical token or biometric data cannot be easily used to log into users’ profiles. The simplest alternative is complementing the single factor (password-based) authentication process with additional identification elements, such as one-time PIN codes, generated by the user’s own device (e.g. the smart phone) or received via SMS. Proposed a brightness based authentication mechanism (i.e., Bright Pass) capable of enhancing the security of identity confirmation PIN codes without asking the user to memorize an additional secret value or to solve a complex cognitive task. This method introduces a new input value that is changed at every usage combining a something you know element (i.e., the PIN) with an interface element that cannot be captured by spyware, i.e., a bright or dark circle displayed on the phone screen to tell the user when to digit the correct PIN digit and when to digit a fake one. It prevents the malware from correctly inserting the PIN code, thereby disallowing the possibility to perform critical operations without the user’s agreement. Proposed work also focuses on implementing secure face recognition approach for user authentication. This approach will enhance the performance of ATM system

Enhancing Smart Home Security with Face Recognition using Deep Learning

Abstract: Recently, security has been a growing concern for human life, and the cost is a significant concern. The research recommends implementing a real-time recognition system to rapidly handle photographs to reduce monitoring expenses and ensure safety in homes and offices by identifying people. The main goal of this study was to create an intelligent face recognition approach using deep learning for intelligent homes. To illustrate the study's effectiveness, the suggested model is contrasted with other cutting-edge techniques. The research introduces a tree-based deep model for facial recognition in the Cloud, which requires fewer computing resources but maintains accuracy. The input volume of the model is partitioned into multiple volumes, and trees are created for each volume according to their height and number of branches. A residual function, constructed from a convolutional layer and two non-linear functions, represents each branch. The proposed model is assessed on various publicly accessible databases and compared with the industry's best deep models for facial recognition.

Analysing the brain networks corresponding to the facial contrast-chimeras.

How humans recognise faces and objects effortlessly, has become a great point of interest. To understand the underlying process, one of the approaches is to study the facial features, in particular ordinal contrast relations around the eye region, which plays a crucial role in face recognition and perception. Recently the graph-theoretic approaches to electroencephalogram (EEG) analysis are found to be effective in understating the underlying process of human brain while performing various tasks. We have explored this approach in face recognition and perception to know the importance of contrast features around the eye region. We studied functional brain networks, formed using EEG responses, corresponding to four types of visual stimuli with varying contrast relationships: Positive faces, chimeric faces (photo-negated faces, preserving the polarity of contrast relationships around eyes), photo-negated faces and only eyes. We observed the variations in brain networks of each type of stimuli by finding the distribution of graph distances across brain networks of all subjects. Moreover, our statistical analysis shows that positive and chimeric faces are equally easy to recognise in contrast to difficult recognition of negative faces and only eyes.

An investigational FW-MPM-LSTM approach for face recognition using defective data

Facial recognition systems are based on the features and traits of the face, since the systems are classified as biometric systems. Additionally, they are founded on the image processing, machine vision and machine learning principles. From images, imperfect information is considered by face recognition systems. A variety of image reconstruction mechanisms is vital in this situation in order to match faces. The proposed method calls for image enhancement at the pre-processing stage. Following the image segmentation and reconstruction stage, the best facial features are extracted using features such the eyes, cheeks, face area and lips. By means of fractal model and wavelet transform the operation is performed. Using the Moore Penrose Matrix, the LSTM neural network is then improved also known as the MPM-LSTM, to train and test the system. From experimental results, the outcomes show that the proposed methodology performs better than the contemporary techniques.

An ensemble face recognition mechanism based on three-way decisions

The explainable human–computer interaction (HCI) is about designing approaches capable of using cognitive characteristics like humans. One such characteristic is human vision and its accuracy. The accuracy measures the trust in that system. Therefore, improving accuracy in the authorization with identification process is a primary concern for a visual-based explainable human–computer interaction system. In this article, we propose a three-way decision based ensembled face recognition mechanism called E3FRM. The E3FRM uses a three-way approach to determine the match cases and the respective worth of the captured image with the match cases. Features are extracted using PCA/FLD, and the ensembled face recognition algorithms utilize the extracted features to process the image. Ensemble Face recognition approaches find the match cases based on a given threshold. Finally, the three-way decision model evaluates the suitability of the captured image for acceptance, rejection, or deferred cases with a dual verification mechanism. Experimental results on well-known eighteen datasets suggest improvements in commonly used metrics of F1, Accuracy and Recall by up to 0.8% to 12.8%, 1% to 9.6% and 1.2% to 13.9%, respectively, in comparison to the state-of-the-art methods available, including SPCA +, ML-EM, FLDA-SVD, DMMA, Fast-DMMA, LU, LPP, TDL, KCFT, RBF + DT, and NMF. Furthermore, the proposed approach is comparatively analyzed with ensembled face recognition methods that result in an outperformed F1, Accuracy and Recall by up to 1.1% to 10.3%, 0.1% to 7.3% and 0.9% to 10.5%, respectively. These results suggest that the proposed model may improve face recognition accuracy and the resulting trust in the machines.

Attractive or Aggressive? A Face Recognition and Machine Learning Approach for Estimating Returns to Visual Appearance

This paper provides a methodological contribution by illustrating the use of computer vision and machine learning methods to identify facial characteristics for the study of facial characteristics in economics. We analyze facial appearance premia for head football coaches at big-time college sports programs to illustrate this methodology. Specifically, we estimate facial attractiveness and aggressiveness premia using quantitative measures of these characteristics from a neural network approach applied to observable facial features. Parametric regression results show evidence of a salary discount for attractive employees along with evidence of an aggressiveness premium. Nonparametric gradient results provide similar qualitative implications.

FaceScan: Unmasking Identity Beyond the Surface

Biometrics, focusing on human behavior and features, encompasses face recognition as a vital technique. This paper offers a survey of face recognition methods, analyzing algorithms and approaches. Face recognition, crucial for security, is explored, emphasizing its computer-based recognition mechanisms. The paper provides an overview of face recognition methodology, recent techniques, and their efficacy under various conditions. Investigating potential biases in current deep learning systems, particularly towards children, reveals performance disparities. Strategies to mitigate this bias are evaluated, identifying effective fusion techniques. Recent advancements in face recognition, fueled by technological progress, have made automated systems practical and widely applicable. This review assesses the strengths, weaknesses, and current/future applications of face recognition technology, including IBM's system and its domains of application.

Designing face resemblance technique using near set theory under varying facial features.

Near sets (also called Descriptively Near Sets) classify nonempty sets of objects based on object feature values. The Near Set Theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way humans perceive the similarity of objects. This paper presents a novel approach for face recognition using Near Set Theory that takes into account variations in facial features due to varying facial expressions, and facial plastic surgery. In the proposed work, we demonstrate two-fold usage of Near set theory; firstly, Near Set Theory as a feature selector to select the plastic surgery facial features with the help of tolerance classes, and secondly, Near Set Theory as a recognizer that uses selected prominent intrinsic facial features which are automatically extracted through the deep learning model. Extensive experimentation was performed on various facial datasets such as YALE, PSD, and ASPS. Experimentation demonstrates 93% of accuracy on the YALE face dataset, 98% of accuracy on the PSD dataset, and 98% of accuracy on the ASPS dataset. A detailed comparative analysis of the proposed work of facial resemblance with other state-of-the-art algorithms is presented in this paper. The experimentation results effectively classify face resemblance using Near Set Theory, which has outperformed several state-of-the-art classification approaches.

A low-resolution real-time face recognition using extreme learning machine and its variants

ABSTRACT Extreme Learning Machine (ELM) is an excellent candidate for its exemplary performance as a generalized Single Layer Feed-forward Network (SLFN). In this regard, ELM has attracted widespread attention for tackling multiclass classification and regression problems with relative ease. Due to the random allocation of weights to the input and hidden layer biases, the running time spans of ELM are observed to be in seconds and agree with real-time events whilst giving a competitive performance. In this work, the suitability of ELM and its Online Sequential variant for recognition of unknown face samples is investigated under uncontrolled environments, including varying pose, variance, and illumination. The presented face recognition approach is evaluated sing four datasets from YALE, CMU, BIOID, and LFW. To track the face features of a sample face in its spatial domain with varying windows corresponding to different datasets, the Viola-Jones object detection framework is used. The face features are then extracted and evaluated using a Histogram of Oriented Gradients, which forms the dataset to be fed to ELM and OS-ELM classifiers. The running time for training the proposed face recognition approach is found to span within milliseconds to seconds with the computed time complexity of , justifying the suitability of ELM and OS-ELM classifiers for real-time face recognition applications with a high recognition rate outcome.

AutoFPR: An Efficient Automatic Approach for Facial Paralysis Recognition Using Facial Features

Facial paralysis (FP) is the most common illness. Nerve damage can cause the affected muscles of the face to lose control. Most FP diagnosis systems heavily depend on skilled clinicians and lack automatic quantitative assessment. This paper introduces a novel automatic facial paralysis recognition (autoFPR) approach, a four-stage machine learning solution, for classifying FP and healthy subjects. Our solution includes the creation of the dataset, feature extraction, dimensionality reduction, and classification. The FP and healthy subject videos are collected from the publicly available YouTube Facial Palsy database and the 300VW database, respectively. Facial features are extracted from the collected facial videos using landmarks, action units, eye gaze, and head pose. Five different experiments were performed to find the best facial paralysis recognition technique. autoFPR can distinguish subjects with facial paralysis and healthy individuals by using movement changes in their eyebrows, eyes, and corners of the mouth. Our goal is to prove that machine learning classifiers are capable of diagnosing FP. We evaluate system performance using accuracy, precision, recall, and F1. Feature selection plus principal component analysis with support vector machine achieved the highest accuracy, with 97.3% for the extracted features.

Human Face Emotions Recognition from Thermal Images Using DenseNet

In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

A two-phase approach for expression invariant 3D face recognition using fine-tuned VGG-16 and 3D-SIFT descriptors

A two-phase approach for expression invariant 3D face recognition using fine-tuned VGG-16 and 3D-SIFT descriptors

Recent Advances in Infrared Face Analysis and Recognition with Deep Learning

Besides the many advances made in the facial detection and recognition fields, face recognition applied to visual images (VIS-FR) has received increasing interest in recent years, especially in the field of communication, identity authentication, public safety and to address the risk of terrorism and crime. These systems however encounter important problems in the presence of variations in pose, expression, age, occlusion, disguise, and lighting as these factors significantly reduce the recognition accuracy. To prevent problems in the visible spectrum, several researchers have recommended the use of infrared images. This paper provides an updated overview of deep infrared (IR) approaches in face recognition (FR) and analysis. First, we present the most widely used databases, both public and private, and the various metrics and loss functions that have been proposed and used in deep infrared techniques. We then review deep face analysis and recognition/identification methods proposed in recent years. In this review, we show that infrared techniques have given interesting results for face recognition, solving some of the problems encountered with visible spectrum techniques. We finally identify some weaknesses of current infrared FR approaches as well as many future research directions to address the IR FR limitations.

A Deep Learning Approach to Predict Chronological Age.

Recently, researchers have turned their focus to predicting the age of people since numerous applications depend on facial recognition approaches. In the medical field, Alzheimer's disease mainly depends on patients' ages. Multiple methods have been implemented and developed to predict age. However, these approaches lack accuracy because every image has unique features, such as shape, pose, and scale. In Saudi Arabia, Vision 2030, concerning the quality of life, is one of the twelve initiatives that were launched recently. The health sector has gained increasing attention as the government has introduced age-based policies to improve the health of its elderly residents. These residents are urgently advised to vaccinate against COVID-19 based on their age. In this paper, proposing a practical, consistent, and trustworthy method to predict age is presented. This method uses the color intensity of eyes and a Convolutional Neural Network (CNN) to predict age in real time based on the ensemble of CNN. A segmentation algorithm is engaged since the approach takes its input from a video stream or an image. This algorithm extracts data from one of the essential parts of the face: the eyes. This part is also informative. Several experiments have been conducted on MATLAB to verify and validate results and relative errors. A Kaggle website dataset is utilized for ages 4 to 59. This dataset includes over 270,000 images, and its size is roughly 2 GB. Consequently, the proposed approach produces ±8.69 years of Mean Square Error (MSE) for the predicted ages. Lastly, a comparative evaluation of relevant studies and the presented algorithm in terms of accuracy, MSE, and Mean Absolute Error (MAE) is also provided. This evaluation shows that the approach developed in the current study outperforms all considered performance metrics since its accuracy is 97.29%. This study found that the color intensity of eyes is highly effective in predicting age, given the high accuracy and acceptable MSE and MAE results. This indicates that it is helpful to utilize this methodology in real-life applications.

Quaternion Scalar and Vector Norm Decomposition: Quaternion PCA for Color Face Recognition.

This paper proposes a decomposition called quaternion scalar and vector norm decomposition (QSVND) for approximation problems in color image processing. Different from traditional quaternion norm approximations that are always the single objective models (SOM), QSVND is adopted to transform the SOM into the bi-objective model (BOM). Furthermore, regularization is used to solve the BOM problem as a common scalarization method, which converts the BOM into a more reasonable SOM. This can handle over-fitting or under-fitting problems neglected in this kind of research for quaternion representation (QR) in color image processing. That is how to treat redundancy caused by the extra scalar part when the vector part of a quaternion is used to represent a color pixel. We apply QSVND to quaternion principal component analysis (QPCA) for color face recognition (FR), which can deal with the phenomenon of under-fitting of vector part norm approximation. Comparisons with the competing approaches on AR, FERET, FEI, and KDEF&AKDEF databases consistently show the superiority of the proposed approach for color FR.

Analytical Study of Face Recognition Methods Using Machine Learning

Many facial recognition algorithms, as well as their adaptations, have previously been devised during the last decades. A number is indicated for standard algorithms. They are divided into two categories: appearance-based and model-based. Three distinct linear subspace analysis strategies for appearance-based processes are presented, along with a brief examination of numerous non-linear manifold analysis approaches for face recognition. Elastic Bunch Graph matching, Active Appearance Model, and 3D Model processes are discussed. A vast variety of publicly available face datasets and performance evaluation ratings are examined. Based on previous recognition outcomes, more study areas are recommended. Face recognition has piqued the interest of academics in biometrics, pattern recognition, and computer vision. Previous research in this field had a basic fault in that it was designed to recognize just single faces, resulting in a long detection time and additional time for annotation. The goal of this study is to create a face recognition system that makes use of a machine learning algorithm and principal component analysis (PCA). The study makes use of linear discriminant analysis, multilayer perceptrons, Naive Bayes, and support vector machines. Furthermore, it obtained 97% and 100% identification accuracy, respectively, using PCA and linear discriminant analysis. The extensive comprehension CNNs are the most frequent form of machine learning algorithm used for facial recognition. CNNs are a sort of artificial neural network that performs exceptionally well at image categorization. Face recognition is a popular topic in artificial intelligence. This program was widely used in our daily life. Several face recognition devices were installed on phones to protect private information and utilized on Facebook to quickly recognize when Facebook users appeared in photographs. Several face recognition algorithms have previously been developed; yet, it remains incredibly difficult in real-world situations. A basic technique for differentiating people is influenced by factors such as partial facial occlusion, illumination, and posture variance.