Face Recognition Applications Research Articles

From facial expressions to algorithms: a narrative review of animal pain recognition technologies.

Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement. Notably, computer vision and machine learning have revolutionized our approach to identifying and addressing pain in non-verbal patients, including animals, with profound implications for both veterinary medicine and animal welfare. By leveraging the capabilities of AI algorithms, we can construct sophisticated models capable of analyzing diverse data inputs, encompassing not only facial expressions but also body language, vocalizations, and physiological signals, to provide precise and objective evaluations of an animal's pain levels. While the advancement of APR holds great promise for improving animal welfare by enabling better pain management, it also brings forth the need to overcome data limitations, ensure ethical practices, and develop robust ground truth measures. This narrative review aimed to provide a comprehensive overview, tracing the journey from the initial application of facial expression recognition for the development of pain scales in animals to the recent application, evolution, and limitations of APR, thereby contributing to understanding this rapidly evolving field.

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.

Non-Binary and Trans-Inclusive AI: A Catalogue of Best Practices for Developing Automatic Gender Recognition Solutions

Artificial intelligence (AI) has significantly optimized processes across various sectors, enhancing efficiency and transforming digital interactions. However, as AI becomes more integrated into daily life, concerns about its social impacts and inherent biases have emerged. This study explores how AI technologies, such as facial recognition and Automatic Gender Recognition (AGR), can perpetuate and amplify societal prejudices, especially against transgender and non-binary individuals. The 2018 case of Amazon's Rekognition technology, which exhibited high false positive rates for individuals with dark skin, highlights the risks of algorithmic bias and mass surveillance. Given these challenges, this research performed performed a systematic mapping study of the literature on AI to present an analysis of problems and respective causes brought by facial recognition and AGR applications to trans and non-binary people. In a second phase, we developed and empirically assessed a catalog of 19 best practices for an ethical AI development grounded in Justice, Equity, Diversity, and Inclusion principles. We aim to establish ethical standards that promote inclusivity to trans and non-binary people, mitigating algorithmic discrimination.

Enhancing Facial Recognition Accuracy in Low-Light Conditions Using Convolutional Neural Networks

Facial recognition technology has become increasingly everywhere in various domains, from security and surveillance to personal device authentication. However, its effectiveness can be significantly hindered in low-light conditions, where images often lack sufficient illumination for accurate recognition. This study proposes a novel approach to enhance facial recognition accuracy in low-light conditions using Convolutional Neural Networks (CNNs), Deep Retinex Decomposition Network (DRDN), and CenterFace algorithm. The methodology leverages CNNs for robust feature extraction, while DRDN corrects illumination by decomposing images. CenterFace integrates feature fusion and denoising layers for discriminative facial features and noise mitigation. Experimental results demonstrate a remarkable improvement in recognition performance, exceeding 80% accuracy. This approach showcases the potential of CNN-based methods with advanced techniques to enhance reliability in real-world facial recognition applications, particularly in low-light environments.

Research On Real Time Emotion Recognition Using Digital Image Processing Using ML

Face recognition technology has garnered significant attention in recent years due to its wide range of applications in various fields such as security, surveillance, biometrics, and human-computer interaction. This abstract provides a comprehensive overview of the advancements in face recognition image processing techniques, methodologies, and applications. The abstract begins by elucidating the fundamental concepts underlying face recognition, including feature extraction, dimensionality reduction, and classification algorithms. Various approaches such as Eigenfaces, Fisher faces, and Local Binary Patterns (LBP) are discussed, highlighting their strengths and limitations. Moreover, recent developments in deep learning techniques, particularly convolutional neural networks (CNNs) and Siamese networks, have revolutionized face recognition by achieving remarkable accuracy and robustness. The abstract delves into the architecture and training procedures of these deep learning models, emphasizing their ability to learn discriminative features directly from raw pixel data. Furthermore, the abstract explores the challenges faced by face recognition systems, such as variations in pose, illumination, expression, and occlusion. Techniques for addressing these challenges, including data augmentation, normalization, and adversarial training, are examined.

A log-based non-convex relaxation regularized regression for robust face recognition

Regression methods are widely employed in face recognition applications. The vector-based norm regression model only represents the pixel by pixel correlation and exhibits poor performance with contiguous occlusion. In contrast, methods based on the nuclear norm describe the low-rank structural information, but it is difficult to achieve satisfactory performance when dealing with complex noise. To address this, a log-based non-convex relaxation regularized regression (log-NCRR) model for robust face recognition is proposed in this paper. We adopt the log-based matrix loss without additional parameters to characterize the low-rank part of error image. Considering sparsity, we design a log-based vector loss to describe the sparse part of the error matrix, which achieves a balance between the l0-norm and l1-norm. Additionally, a weighted non-convex relaxation of l2,1-norm is proposed to ensure the group sparsity of the regression coefficient. The optimization problem is optimized by ADMM, with the corresponding sub-problems easily solved by the generalized singular value shrinkage operator. Finally, experimental results on four public databases for various types of noise validate the effectiveness and robustness of log-NCRR in face recognition.

Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration

This paper addresses the limitations of relying solely on facial expressions for emotion recognition by proposing an advanced approach that emphasizes continuous monitoring of electroencephalography (EEG) signals. Recognizing the potential for deception in facial expressions, our study leverages the growing interest in EEG signals, tapping into advancements in deep learning and machine learning. By optimizing the configuration of EEG electrodes, our approach enhances the accuracy of emotion classification systems, offering a streamlined solution. The proposed multi-input system refines EEG-based emotion recognition efficiency and integrates facial expression analysis to enhance overall system effectiveness. Through the application of brain heat map topographies and facial expression recognition, our system, employing just nine electrodes, outperforms basic emotion recognition setups. Experimental results validate that combining facial expression analysis with EEG signals provides a more comprehensive and accurate understanding of human emotions. This innovative approach holds significance across various sectors, including healthcare, psychology, and human–computer interaction. The paper introduces a novel multi-input system approach, collaboratively fusing two powerful deep learning algorithms: two Convolutional Neural Networks (CNNs). The proposed EEG-based CNN algorithm achieves an efficiency of 87.43%, rising to 91.21% when integrated with the DeepFace CNN. The seamless integration of facial expressions and brain topographies enables the system to efficiently harness abundant information from both modalities, ensuring a thorough comprehension of human emotions. By capitalizing on the combined advantages of analyzing facial expressions and EEG-derived brain topography, this avant-garde technique substantially improves both precision and efficiency in emotion recognition systems. This enhancement establishes a foundation for the introduction of innovative applications across a spectrum of fields.

Studies Advanced in Development and Application of Facial Expression Recognition

Facial expression is an important way to convey people's inner emotional changes, as the basis of emotional understanding and the prerequisite for computers to understand human emotions, expression recognition has attracted more and more research attention from academia and industry in recent years. Most of the early expression recognition methods relied on manual features such as texture, geometry, and contour. Thanks to the powerful feature representation capability of convolutional neural networks, deep learning-based face recognition technology has made breakthroughs in recognition accuracy and speed. This paper details the latest research progress in the field of face expression recognition, including the design ideas, key steps, advantages and disadvantages of representative methods. This paper also introduces the common face expression recognition datasets and quantitatively compares the results of different methods on these datasets. Finally, this paper discusses the problems in the face expression recognition research field and look forward to the future development.

One level deep convolutional neural network for facial key points detection

Facial landmark detection has a lot of applications in face recognition, face alignment, facial expression recognition, video surveillance and security systems. In the existing literature, there are multiple methods utilizing convolutional neural networks (CNNs) that address this problem in various ways. In many cases, the models use a tree-like structure of CNNs to achieve better results. This paper proposes a combination of three parallel deep convolutional neural networks (DCNNs) to estimate the accurate localization of each keypoint. The first one focuses on the whole face to outperform five points, including the eyes, nose, and mouth corners. The second one focuses on the eyes-nose parts to outperform three points, specifically the eyes and nose. The last one focuses on the nose-mouth parts to outperform three points, namely the nose and mouth corners. Further, we combine all outputs of the three DCNNs and take the average value of each detected key point as the final output. In the first step, we improvthe the parameter efficiency and accuracy of each DCNNs through a set of experiments using the labeled face parts in-the-wild database (LFPW) and the helen facial feature dataset (Helen). Then, we demonstrate that our approach yields more accurate estimations of facial key points than two state-of-the-art methods and commercial software in terms of accuracy.

Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning.

Personal privacy protection has been extensively investigated. The privacy protection of face recognition applications combines face privacy protection with face recognition. Traditional face privacy-protection methods encrypt or perturb facial images for protection. However, the original facial images or parameters need to be restored during recognition. In this paper, it is found that faces can still be recognized correctly when only some of the high-order and local feature information from faces is retained, while the rest of the information is fuzzed. Based on this, a privacy-preserving face recognition method combining random convolution and self-learning batch normalization is proposed. This method generates a privacy-preserved scrambled facial image and an image fuzzy degree that is close to an encryption of the image. The server directly recognizes the scrambled facial image, and the recognition accuracy is equivalent to that of the normal facial image. The method ensures the revocability and irreversibility of the privacy preserving of faces at the same time. In this experiment, the proposed method is tested on the LFW, Celeba, and self-collected face datasets. On the three datasets, the proposed method outperforms the existing face privacy-preserving recognition methods in terms of face visual information elimination and recognition accuracy. The recognition accuracy is >99%, and the visual information elimination is close to an encryption effect.

On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition

With the rise of deep neural networks, the performance of biometric systems has increased tremendously. Biometric systems for face recognition are now used in everyday life, e.g., border control, crime prevention, or personal device access control. Although the accuracy of face recognition systems is generally high, they are not without flaws. Many biometric systems have been found to exhibit demographic bias, resulting in different demographic groups being not recognized with the same accuracy. This is especially true for facial recognition due to demographic factors, e.g., gender and skin color. While many previous works already reported demographic bias, this work aims to reduce demographic bias for biometric face recognition applications. In this regard, 12 face recognition systems are benchmarked regarding biometric recognition performance as well as demographic differentials, i.e., fairness. Subsequently, multiple fusion techniques are applied with the goal to improve the fairness in contrast to single systems. The experimental results show that it is possible to improve the fairness regarding single demographics, e.g., skin color or gender, while improving fairness for demographic subgroups turns out to be more challenging.

New Face Recognition System Based on DCT Pyramid and Backpropagation Neural Network

Face recognition has emerged as a prominent biometric identification technique with applications ranging from security to human-computer interaction. This paper proposes a new face recognition system by appropriately combining techniques for improved accuracy. Specifically, it incorporates a discrete cosine transform (DCT) pyramid for feature extraction, statistical measures for dimensionality reduction of the features, and a two-layer backpropagation neural network for classification. The DCT pyramid is used to effectively capture both low- and high-frequency information from face images to improve the ability of the system to recognise faces accurately. Meanwhile, the introduction of statistical measures for dimensionality reduction helps in decreasing the computational complexity and provides better discrimination, leading to more efficient processing. Moreover, the two-layer neural network introduced, which plays a vital role in efficiently handling complex patterns, further enhances the recognition capabilities of the system. As a result of these advancements, the system achieves an outstanding 99 % recognition rate on the Olivetti Research Laboratory (ORL) data set, 98.88 % on YALE, and 99.16 % on AR. This performance demonstrates the robustness and potential of the proposed system for real-world applications in face recognition.

Image-Based Face Recognition Techniques Used in Disease Detection Approaches: A Survey

Facial diseases lead to noticeable changes on the human face, and some of them extend beyond internal effects or organ-based disorders. Indeed, certain types of facial diseases result in visually noticeable abnormalities on the human face. These alterations in facial patterns can serve as potential indicators for corresponding diseases, particularly in the fields of endocrinology and metabolism, Muscles-Nervous disorders, Chromosomes, and Genetic disorders, among others. Technologies used in Face Recognition (FR) have been developed over the past few decades; however, only a limited amount of research has been applied in recent years to FR-based disease detection for clinical purposes. FR applications relying on Artificial Neural Network (ANN) techniques have recorded higher accuracy rates in diagnosing facial diseases. This field of recognition holds promising potential for optimizing facial diagnosis approaches and supporting medical staff in evaluating detections. In practice, only a few research ideas have been translated into medical products, emphasizing the need to identify and integrate future applications. As a primary focus, this paper centers on the key applications and technologies for detecting various types of facial diseases, along with a discussion of prospects.

A mixed-methods investigation of the factors affecting the use of facial recognition as a threatening AI application

PurposeArtificial intelligence (AI) applications pose a potential threat to users' data security and privacy due to their high data-dependence nature. This paper aims to investigate an understudied issue in the literature, namely, how users perceive the threat of and decide to use a threatening AI application. In particular, it examines the influencing factors and the mechanisms that affect an individual’s behavioral intention to use facial recognition, a threatening AI.Design/methodology/approachThe authors develop a research model with trust as the key mediating variable by integrating technology threat avoidance theory, the theory of planned behavior and contextual factors related to facial recognition. Then, it is tested through a sequential mixed-methods investigation, including a qualitative study (for model development) of online comments from various platforms and a quantitative study (for model validation) using field survey data.FindingsPerceived threat (triggered by perceived susceptibility and severity) and perceived avoidability (promoted by perceived effectiveness, perceived cost and self-efficacy) have negative and positive relationships, respectively, with an individual’s attitude toward facial recognition applications; these relationships are partially mediated by trust. In addition, perceived avoidability is positively related to perceived behavioral control, which along with attitude and subjective norm is positively related to individuals' intentions to use facial recognition applications.Originality/valueThis paper is among the first to examine the factors that affect the acceptance of threatening AI applications and how. The research findings extend the current literature by providing rich and novel insights into the important roles of perceived threat, perceived avoidability, and trust in affecting an individual’s attitude and intention regarding using threatening AI applications.

Computationally intelligent real-time security surveillance system in the education sector using deep learning.

Real-time security surveillance and identity matching using face detection and recognition are central research areas within computer vision. The classical facial detection techniques include Haar-like, MTCNN, AdaBoost, and others. These techniques employ template matching and geometric facial features for detecting faces, striving for a balance between detection time and accuracy. To address this issue, the current research presents an enhanced FaceNet network. The RetinaFace is employed to perform expeditious face detection and alignment. Subsequently, FaceNet, with an improved loss function is used to achieve face verification and recognition with high accuracy. The presented work involves a comparative evaluation of the proposed network framework against both traditional and deep learning techniques in terms of face detection and recognition performance. The experimental findings demonstrate that an enhanced FaceNet can successfully meet the real-time facial recognition requirements, and the accuracy of face recognition is 99.86% which fulfills the actual requirement. Consequently, the proposed solution holds significant potential for applications in face detection and recognition within the education sector for real-time security surveillance.

A model study of teaching method reform of computer laboratory course integrating internet of things technology.

The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.

An Augmented Classroom Teaching System based on AR and Facial Recognition

The goal of this study is to investigate the perception of the application of facial recognition and augmented reality in the classroom. HoloLens 2, an AR device, and Microsoft Azure cognitive services were utilized to enhance teachers' classroom perception by displaying learning analytics information on the AR device in real time. A focus group interview was conducted to collect initial feedback from teachers and students regarding the benefits, challenges, and design concerns related to the proposed system. A refined interface was designed based on the feedback from the participants.

Max-C and Min-D Projection Auto-Associative Fuzzy Morphological Memories: Theory and an Application for Face Recognition

Max-C and min-D projection auto-associative fuzzy morphological memories (max-C and min-D PAFMMs) are two-layer feedforward fuzzy morphological neural networks designed to store and retrieve finite fuzzy sets. This paper addresses the main features of these auto-associative memories: unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and excellent tolerance to either dilative or erosive noise. Particular attention is given to the so-called Zadeh’ PAFMM, which exhibits the most significant noise tolerance among the max-C and min-D PAFMMs besides performing no floating-point arithmetic operations. Computational experiments reveal that Zadeh’s max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with a strong potential for face recognition tasks.

Retracted: Application of Face Recognition in E-commerce Security Authentication in the Era of Big Data

Retracted: Application of Face Recognition in E-commerce Security Authentication in the Era of Big Data

Retracted: Application of High-Resolution Face Recognition and EDF Image Reconstruction in English Classroom Teaching

Retracted: Application of High-Resolution Face Recognition and EDF Image Reconstruction in English Classroom Teaching