Face Recognition Applications Research Articles

A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face Recognition

This study provides a comparative evaluation of face recognition databases and Convolutional Neural Network (CNN) architectures used in training and testing face recognition systems. The databases span from early datasets like Olivetti Research Laboratory (ORL) and Facial Recognition Technology (FERET) to more recent collections such as MegaFace and Ms-Celeb-1M, offering a range of sizes, subject diversity, and image quality. Older databases, such as ORL and FERET, are smaller and cleaner, while newer datasets enable large-scale training with millions of images but pose challenges like inconsistent data quality and high computational costs. The study also examines CNN architectures, including FaceNet and Visual Geometry Group 16 (VGG16), which show strong performance on large datasets like Labeled Faces in the Wild (LFW) and VGGFace, achieving accuracy rates above 98%. In contrast, earlier models like Support Vector Machine (SVM) and Gabor Wavelets perform well on smaller datasets but lack scalability for larger, more complex datasets. The analysis highlights the growing importance of multi-task learning and ensemble methods, as seen in Multi-Task Cascaded Convolutional Networks (MTCNNs). Overall, the findings emphasize the need for advanced algorithms capable of handling large-scale, real-world challenges while optimizing accuracy and computational efficiency in face recognition systems.

Application of Facial Emotion Recognition in Teaching-Learning Process for Quality Assessment and Enhancement

Application of Facial Emotion Recognition in Teaching-Learning Process for Quality Assessment and Enhancement

Exploring the Challenges of Aadhaar based Face Recognition in Unrestricted Environments

- Technology improvements have resulted in a high criminal rate, which has raised serious concerns about security-related issues. Face recognition can be used to identify people because every person tends to possess a particular attribute. One of the main applications of facial recognition is in the field of video surveillance. To reduce the increasing criminal rates, this technology is responsible for extracting features from the human face and further identifying them. The CCTV footage might also be used to identify suspects at a crime scene. However, the criminals would be found by recognizing and comparing the suspect's face to the database repository that would include data on people based on their Aadhaar card. Aadhaar photos can be used to identify and recognize faces in a facial recognition system since Aadhaar is a unique identification system in India and mandatory for every Indian resident. However, there are many challenges faced by Aadhaar based face recognition. Face recognition in uncontrolled environments like public surveillance faces significant challenges such as low-resolution images, pose variations, and masked faces. This paper highlights the challenges faced by Aadhaar enabled face recognition in surveillance scenes. This article aims to provide a brief survey of the Aadhaar-enabled face recognition system for criminal identification and recognizing individuals. There is limited work with the Aadhaar dataset due to privacy and security reasons. This article also emphasizes the difficulties that will be encountered in implementing facial recognition as well as strategies for enhancing it while taking various tradeoffs into account. It also recommends opportunities for further study in the application of facial identification in other fields. Key Words: Aadhaar, Surveillance, Face recognition, Criminals

Pain-Related Responses in Preterm Babies Using Automated and Laser Heel-Lancing Devices.

Purpose: This study aimed to compare the pain-related behavioral and physiological responses and puncture-related characteristics between automated and laser lancets and examine the correlation between infants' pain-related responses and facial expressions during heel lancing in a neonatal intensive care unit. Methods: Pain-related responses were assessed using the Neonatal Infant Pain Scale, heart rate, and oxygen saturation through pulse oximetry. Facial expressions were also analyzed using a facial recognition application. Puncture-related characteristics were assessed through puncture marks, number of punctures, and hemostasis. Results: There was a significantly higher puncture mark frequency at 3 min in the automated lancet group than in the laser lancet group. There were no interaction effects of time and group on pain-related behavioral response, heart rate, or oxygen saturation. There were significant positive correlations between the pain-related behavioral response and anger, disgust, fear, and sadness. Conclusion: The laser lancet had relatively good outcomes regarding puncture-related characteristics, although there was no significant difference in pain-related responses. A laser lancet is a novel option for preterm babies at risk of scar-related inflammation and coagulopathies.

Comprehensive Analysis of Face Recognition Technologies

This article provides a comprehensive review of face recognition research, focusing on advancements made over the past century. It presents a detailed examination of the core concepts, principles, steps, and classifications of face recognition technology. The review highlights the practical applications of face recognition in contemporary contexts and summarizes key datasets and preprocessing methods used in the field. The paper categorizes face recognition methods into three main types and places particular emphasis on hybrid methods. It explores the principles and research processes associated with these methods, offering an in-depth analysis of their results. Among the various techniques reviewed, deep learning methods emerge as the most promising for face recognition due to their superior performance. This review serves as a valuable resource for students and novice researchers by providing a clear overview of current research methodologies and tools. Additionally, it outlines potential research directions and contributes to the advancement of the field of computer vision.

A Text-Free Training Method for Generating Face Images Based on Chinese Text

Generative adversarial network (GAN) technology has enabled the automatic synthesis of realistic face images from text. This paper proposes a model for generating face images from Chinese text by integrating a text mapping module with the StyleGAN generator. The text mapping module utilizes the CLIP model for pre-training Chinese text, employs a convolutional-inverse convolutional structure to enhance feature extraction, and incorporates a BiLSTM model to construct complete sentences as inputs for the StyleGAN generator. The generator interprets semantic features to generate face images. Validation on Face2Text and COCO datasets yields F1 values of 83.43% and 84.97%, respectively, while achieving the lowest FID and FSD scores of 103.25 and 1.26. The combination of CLIP pre-training and word-level semantic embedding improves image quality, offering a novel approach for face recognition applications in public safety.

A TWO-STEP AUTHENTICATION FACIAL RECOGNITION SYSTEM FOR AUTOMATED ATTENDANCE TRACKING

This study addresses the need for efficient, automated attendance systems through the design of a facial recognition application. Manual attendance systems are slow, error-prone and the retrieval of old records can be tedious. Universally assessable technological developments such as facial recognition software can easily solve these problems. However, the vast amount of computational resources required for its implementation has posed a limitation to its wide adoption. This study presents a two-step approach to resolve these challenges. By leveraging a faster, less-powerful model, as the first step, the workload of facial recognition can be distributed to save time and computational cost. A more powerful machine learning model is applied as the second step, deployed for tasks that are too complex for the first model to handle. The two-step authentication process will also reduce the occurrences of false negatives. Face_recognition, a python library is used for detection and encoding of face images read using python’s opencv library from an IP webcam. A flask application demonstrates this facial recognition functionality. The database connection and communication are accomplished using flask_sqlalchemy. A graphical user interface (web application) is used to interact with users on a high level, showing saved images of logged personnel and their times of entry. The system has a maximum accuracy of 98.78% and precision of 98.82% from tests. This shows its potential for application on a wider scale, with some added improvements such as cloud deployment and larger datasets.

A Review of Traditional Methods and Deep Learning for Face Recognition

Face recognition technology is a key research direction in artificial intelligence and computer vision. It first originated in the 1960s, when Bledsoe et al. proposed and studied computer programming based on face recognition. It is divided into three steps: face detection, feature extraction, and face verification. This paper briefly introduces the wide application of face recognition in various fields such as vulnerable groups, analyses its characteristics and progress from the viewpoints of traditional learning and deep learning, and focuses on the methods based on face feature extraction (including geometric features, texture features, local features, etc.). From the previous studies appear some problems connected to environmental changes, such as light change, facial occlusion, posture and privacy leakage, etc. This paper will give the possible reasons for these problems and give the possible development direction of the future by exploring the current face recognition technology to provide a reference for the related research of face recognition technology.

APLIKASI PENGENALAN WAJAH UNTUK ABSENSI PADA SMA DUA MEI CIPUTAT MENGGUNAKAN METODE HAAR-CASCADE CLASSIFIER BERBASIS WEBSITE

The attendance application is one important system that must be possessed by a school in order to manage student attendance data. Some schools still use manual methods for student attendance by using signatures or calling them one by one. Manual attendance is considered inefficient because the risk of losing attendance sheets is very easy and parents cannot monitor whether their child is attending school or not. This is directly felt by SMA Dua Mei Ciputat with the presence of students who left home but did not attend school. Therefore, this research aims to assist teachers and parents in monitoring students to be more disciplined in coming to school by building a face recognition application for attendance using the haar-cascade classifier method based on a website. The haar-cascade classifier method has the advantages of computation in face detection and the local binary pattern histogram algorithm which has good accuracy in face recognition. The results of this research show that the haar-cascade classifier method can be used to create a face recognition application for attendance with fairly good accuracy, namely 100% with 5 attempts with recognition of one face, 100% with 5 attempts with recognition of two faces, and 20% with 5 attempts with recognition of three faces. With these results, the application is only capable of recognizing two faces simultaneously in the attendance process.

IMPLEMENTASI ALGORITMA KNN UNTUK DETEKSI GENDER DAN USIA BERBASIS PENGENALAN WAJAH

In the modern era, face recognition is one of the important technologies used in various security and data analysis applications. This paper aims to use Python to implement the K-Nearest Neighbors (KNN) algorithm to detect gender and age through face recognition. The main problem faced is the accuracy in classifying gender and estimating age from various facial images with lighting, pose, and expression. This study uses various steps to collect facial data, use image processing techniques to extract features, and use the KNN algorithm for classification. The results show that applying KNN can detect gender and age from faces with sufficient accuracy, although there are some problems with extreme lighting and pose conditions. These findings indicate that the KNN algorithm can be used in facial recognition applications to detect gender and age. By fixing parameters and improving data quality, further improvements can be achieved.

Application of Convolutional Neural Network in Image Recognition

Image recognition is of great significance for both human social development and daily life. This paper mainly discusses the application research of convolutional neural network (CNN) in the field of image recognition. Firstly, the basic structure and working principle of CNN, including the core components of convolution layer, pooling layer and full connection layer, are introduced, and then the structure and characteristics of the middle layer are explained. Then, this paper compares the advantages and disadvantages of different CNN models such as LeNet-5, AlexNet, GoogLeNet, ResNet, etc., and analyzes their application in face recognition, medical image recognition, traffic recognition, character recognition and other fields. Finally, this paper summarizes the application of CNN in image recognition, and points out the problems faced in its practical application, such as model complexity, computational resource requirements, etc., aiming to provide reference for further research of image recognition technology.

Retraction Note: Application of facial expression recognition based on domain-adapted convolutional neural network in English smart teaching system

Retraction Note: Application of facial expression recognition based on domain-adapted convolutional neural network in English smart teaching system

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.

Precision sketching with de-aging networks in forensics

Addressing the intricacies of facial aging in forensic facial recognition, traditional sketch portraits often fall short in precision. This study introduces a pioneering system that seamlessly integrates a de-aging module and a sketch generator module to overcome the limitations inherent in existing methodologies. The de-aging module utilizes a deepfake-based neural network to rejuvenate facial features, while the sketch generator module leverages a pix2pix-based Generative Adversarial Network (GAN) for the generation of lifelike sketches. Comprehensive evaluations on the CUHK and AR datasets underscore the system’s superior efficiency. Significantly, comprehensive testing reveals marked enhancements in realism during the training process, demonstrated by notable reductions in Frechet Inception Distance (FID) scores (41.7 for CUHK, 60.2 for AR), augmented Structural Similarity Index (SSIM) values (0.789 for CUHK, 0.692 for AR), and improved Peak Signal-to-Noise Ratio (PSNR) metrics (20.26 for CUHK, 19.42 for AR). These findings underscore substantial advancements in the accuracy and reliability of facial recognition applications. Importantly, the system, proficient in handling diverse facial characteristics across gender, race, and culture, produces both composite and hand-drawn sketches, surpassing the capabilities of current state-of-the-art methods. This research emphasizes the transformative potential arising from the integration of de-aging networks with sketch generation, particularly for age-invariant forensic applications, and highlights the ongoing necessity for innovative developments in de-aging technology with broader societal and technological implications.

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.