Face Recognition Performance Research Articles

Innovative strategies for intelligent services in smart libraries in the information age based on linear discriminant analysis

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

Advancing Face Recognition Technologies: The Role of Decision Trees in Classifying Complex Image Pairs

The advancement of face recognition technologies has been pivotal in various applications, from security systems to personalized user experiences. There are significant efforts already devoted to solving challenges of multimodality and pose variation in face recognition. Some studies focus on multimodality but pose-invariant, and other studies focus on pose variation but single modality. Despite significant progress, various face recognition algorithms do not consider both multimodality and pose variation constraints in their proposed methods. Recognizing face images presented both in a different modality and in a different pose presents serious challenges to current algorithms. This paper proposes an algorithm that combines the strengths of deep learning with decision trees to improve face recognition performance across modalities and poses in constrained and unconstrained environments. This hybrid approach leverages the representational power of deep learning and the interpretability and simplicity of decision trees. The findings indicate significant improvements over existing methodologies, particularly in challenging conditions like when multimodality and pose variation constraints are compounded together in the input face images in both constrained and unconstrained environments. The proposed algorithm not only addresses the limitations of current face recognition systems but also offers scalable, efficient solutions suitable for real-world applications.

Investigating the impact of Gaussian noise on face recognition performance for humans and convolutional neural networks

Investigating the impact of Gaussian noise on face recognition performance for humans and convolutional neural networks

A Comparative Study Recognizing the Expression of Information Between Elderly Individuals and Young Individuals.

Studies have shown that elderly individuals have significantly worse facial expression recognition scores than young adults. Some have suggested that this difference is due to perceptual degradation, while others suggest it is due to decreased attention of elderly individuals to the most informative regions of the face. To resolve this controversy, this study recruited 85 participants and used a behavioral task and eye-tracking techniques (EyeLink 1000 Plus eye tracker). It adopted the "study-recognition" paradigm, and a mixed experimental design of 3 (facial expressions: positive, neutral, negative) × 2 (subjects' age: young, old) × 3 (facial areas of interest: eyes, nose, and mouth) was used to explore whether there was perceptual degradation in older people's attention to facial expressions and investigate the differences in diagnostic areas between young and older people. The behavioral results revealed that young participants had significantly higher facial expression recognition scores than older participants did; moreover, the eye-tracking results revealed that younger people generally fixated on faces significantly more than elderly people, demonstrating the perceptual degradation in elderly people. Young people primarily look at the eyes, followed by the nose and, finally, the mouth when examining facial expressions. The elderly participants primarily focus on the eyes, followed by the mouth and then the nose. The findings confirmed that young participants have better facial expression recognition performance than elderly participants, which may be related more to perceptual degradation than to decreased attention to informative areas of the face. For elderly people, the duration of gaze toward the facial diagnosis area (such as the eyes) should be increased when recognizing faces to compensate for the disadvantage of decreased facial recognition performance caused by perceptual aging.

Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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

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

Facial recognition for disaster victim identification

Mass disaster events can result in high levels of casualties that need to be identified. Whilst disaster victim identification (DVI) relies on primary identifiers of DNA, fingerprints, and dental, these require ante-mortem data that may not exist or be easily obtainable. Facial recognition technology may be able to assist. Automated facial recognition has advanced considerably and access to ante-mortem facial images are readily available. Facial recognition could therefore be used to expedite the DVI process by narrowing down leads before primary identifiers are made available. This research explores the feasibility of using automated facial recognition technology to support DVI. We evaluated the performance of a commercial-off-the-self facial recognition algorithm on post-mortem images (representing images taken after a mass disaster) against ante-mortem images (representing a database that may exist within agencies who hold face databases for identity documents (such as passports or driver's licenses). We explored facial recognition performance for different operational scenarios, with different levels of face image quality, and by cause of death. Our research is the largest facial recognition evaluation of post-mortem and ante-mortem images to date. We demonstrated that facial recognition technology would be valuable for DVI and that the performance varies by image quality and cause of death. We provide recommendations for future research.

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.

AI Face Recognition and Processing Technology Based on GPU Computing

In recent years, with the development of deep neural network technology, real-time object detection has become increasingly common in mobile applications. However, practical application requirements drive the algorithm to optimize in terms of speed, energy consumption and accuracy. This paper introduces the application of artificial intelligence in the field of face recognition, especially using TensorRT accelerated reasoning technology to improve the speed and performance of face recognition. At the same time, the paper also discusses the key role of GPU computing in face recognition, and expounds the importance of AI chips for optimizing inference tasks. Through the analysis of experimental results and methods, the performance advantages and application prospects of BlazeFace algorithm in mobile applications are demonstrated, which provides a valuable reference for industry.

Development of a facial recognition model using optimized convolution neural network with Tiny-ML and fingerprint authentication for safe lock entry

Safe boxes have been used for centuries to protect valuable items, and their design and construction have evolved over time to improve security. In this study, we carried out the design and implementation of a facial recognition technology using optimized convolutional neural networks (CNNs) with TinyML and fingerprint. The facial recognition system is designed using pre-trained CNN architectures then optimized with TinyML to make it fit into resource constrained edge device, the fingerprint recognition is designed using ridge matching biometric algorithm which fits in perfectly into the microcontroller-based system. The safe box was fabricated using sheet metal and insulated using heat resistive material (fiber glass), components like OV2640 camera module, ESP32-S3, R305 Fingerprint sensor, 4.3 inches touch screen TFT display and a charging module to manage and charge the systems battery were used. The system was implemented to be a level two security and two-way verification system using secured Wi-Fi to access the system before the facial and fingerprint recognition functionality is activated, the sequence of operation can either be facial recognition before fingerprint recognition or fingerprint recognition before facial recognition. Evaluating the system’s facial recognition performance with confusion matrix, of all the optimized CNN architectures like VGGNet, SENet, RestNet, MobileNet-V2, EfficientNet-B0, SENet Architecture performed best outputting an accuracy of 98%, precision of 92%, also SENet unoptimized model performed same, MobileNet-V2 performed optimally in the inference speed test with a speed of 15ms/images with the fingerprint also evaluated using confusion matrix and perform with 98% accuracy. It was noticed that the inference speed of the unoptimized model is faster than that of the TinyML optimized model which is justifiable based on the processing clock speed, memory usage, GPU and architecture of the microcontroller/microprocessor. From the result obtained TinyML proves to be suitable mean for edge AI (artificial intelligence) and is recommended for future research and development.

Understanding of facial features in face perception: insights from deep convolutional neural networks.

Face recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process. To address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees. Our findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN. Overall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field.

Beyond the Mask: Advancements in Facial Recognition through Convolutional Neural Networks

Abstract: Facial recognition technology has gained immense popularity in recent years, yet challenges persist with face alterations and the widespread use of masks. Uncooperative individuals in real-world scenarios, such as video surveillance, often resort to masking, leading to a degradation in current face recognition performance. While extensive research has addressed face recognition under various conditions, the impact of masks has often been overlooked. This research focuses specifically on improving the accuracy of recognizing faces obscured by different masks. Our proposed approach involves initial detection of facial regions, addressing the occluded face detection problem through the Multi-Task Cascaded Convolutional Neural Network (MTCNN). Subsequently, facial feature extraction utilizes the Google Face Net embedding model, followed by a classification task executed by Support Vector Machine (SVM). Experimental results demonstrate the effectiveness of this approach in masked face recognition, with noteworthy performance even under excessive facial masks. Additionally, a comparative study enhances our understanding of the proposed method's capabilities

A Comprehensive Framework for Face age Synthesis, Voice Transformation, and Cartoon Generation

This paper presents a comprehensive framework addressing age-invariant face recognition (AIFR), face age synthesis (FAS), voice transformation, and avatar generation. Traditional AIFR techniques focus on minimizing age-related variations in face recognition but lack visual interpretability, while FAS methods aim to synthesize faces of different ages but often compromise recognition due to artifacts. To address these limitations, we propose MTLFace, a multi-task learning framework that simultaneously handles AIFR and FAS tasks. MTLFace employs attention-based feature decomposition to separate identity and age-related features spatially, improving interpretability. Additionally, we introduce an identity conditional module for fine-grained face age synthesis, enhancing the naturalness of synthesized faces. Unlike conventional methods that achieve age group-level synthesis, our identity conditional module enables identity-level synthesis, resulting in smoother age transitions and preserving individual facial characteristics. Leveraging high-quality synthesized faces, we enhance AIFR performance via selective fine-tuning, where synthesized faces are used to augment training data, leading to improved robustness against age variations. Furthermore, we contribute a large cross-age face dataset with annotations, facilitating research in age-related tasks. In addition to AIFR and FAS, we explore limitations in existing methodologies for voice transformation and propose advancements to improve speech quality and address current challenges. Lastly, we introduce a novel approach for cartoon face generation, utilizing component-based facial feature extraction and template matching to produce diverse and stylistic cartoon faces. Experimental results across various benchmarks demonstrate the effectiveness of our approaches in achieving superior performance in face recognition, voice transformation, and cartoon face generation, thereby contributing to the advancement of multimodal synthesis technologies with potential applications in entertainment, virtual communication, and assistive technologies

Relationship between face recognition ability and anxiety tendencies in healthy young individuals: A prosopagnosia index and state-trait anxiety inventory study

Developmental prosopagnosia (DP) is a condition that indicates the inability to recognize individuals by their faces from birth, without any history of brain damage. The assessment of face recognition ability and diagnosis of DP involve the use of face tests such as the Cambridge Face Memory Test (CFMT) and the Cambridge Face Perception Test, along with self-reported measures like the 20-Item Prosopagnosia Index (PI20). Face recognition accuracy is affected by anxiety. However, previous studies on the relationship between face recognition ability and anxiety have not used the PI20 measure. This study aimed to investigate the relationship between self-reported measures of face recognition ability and anxiety tendencies among healthy young individuals for DP diagnosis and its implications. We used a face recognition test, involving the PI20, CFMT, Visual Perception Test for Agnosia–Famous Face Test (VPTA–FFT), and State-Trait Anxiety Inventory (STAI). We assessed the performance of 116 Japanese young adults (75 females, median age of 20.7 years, with a standard deviation of 1.2). Subsequently, we conducted a statistical analysis to examine the relationship between the outcomes of the face recognition tests and STAI scores using Pearson correlation analysis and single correlation coefficients. The results showed a positive correlation between state anxiety and PI20 (r = 0.308, p = 0.007), and a weak positive correlation was also observed between trait anxiety and PI20 (r = 0.268, p = 0.04). In contrast, there was no correlation between CFMT and VPTA–FFT with respect to STAI. The results of the hierarchical multiple regression analysis also suggested that the correlation between the performance on the PI20 (self-report) and objective measures of face recognition performance (the CFMT and the VPTA–FFT) are driven by differences in anxiety. This study is the first to explore the relationship between face recognition abilities and anxiety using the PI20 self-report measure. There are implications for future research on the diagnosis of DP and the relationship between anxiety and face recognition.

MixFace: Improving face verification with a focus on fine‐grained conditions

AbstractThe performance of face recognition (FR) has reached a plateau for public benchmark datasets, such as labeled faces in the wild (LFW), celebrities in frontal‐profile in the wild (CFP‐FP), and the first manually collected, in‐the‐wild age database (AgeDB), owing to the rapid advances in convolutional neural networks (CNNs). However, the effects of faces under various fine‐grained conditions on FR models have not been investigated, owing to the absence of relevant datasets. This paper analyzes their effects under different conditions and loss functions using K‐FACE, a recently introduced FR dataset with fine‐grained conditions. We propose a novel loss function called MixFace, which combines classification and metric losses. The superiority of MixFace in terms of effectiveness and robustness was experimentally demonstrated using various benchmark datasets.

Face Recognition and Model Analysis Using CNN Based on Self-Collected Information

Face recognition helps people to determine the features and characteristics of a face, which in turn helps the police to find criminals, reduce the crime rate and ensure the safety of the society. In order to further improve the performance of face recognition, the main objective of this research is to collect a large amount of face information using Convolutional Neural Networks (CNN) to achieve effective recognition. Specifically, first, a code is used in the annotation to define the coordinates of the face. This function converts the classification labels into numbers that can be understood by the model. Second, generate targets in order to parse the annotation file and obtain objects from it. Third, a CNN model is constructed. The features in the data are captured using the adaptive weights of the CNN. In addition, the predictive performance of different models is analysed and compared. In addition, the objective function will transform the face coordinates of the file attached to the boxed list, which helps to acquire the commanded faces in the image file. It is shown through experiments that the proposed model can perform face recognition effectively. The proposed program in this paper can provide valuable thoughts and insights to the field.

FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protocols. FRCSyn-onGoing is based on the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first face recognition international challenge aiming to explore the use of real and synthetic data independently, and also their fusion, in order to address existing limitations in the technology. Specifically, FRCSyn-onGoing targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. To enhance face recognition performance, FRCSyn-onGoing strongly advocates for information fusion at various levels, starting from the input data, where a mix of real and synthetic domains is proposed for specific tasks of the challenge. Additionally, participating teams are allowed to fuse diverse networks within their proposed systems to improve the performance. In this article, we provide a comprehensive evaluation of the face recognition systems and results achieved so far in FRCSyn-onGoing. The results obtained in FRCSyn-onGoing, together with the proposed public ongoing benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

Literature review on the application of numerical model in improving face recognition performance

As an important biometric technology, the applications of face recognition technology have been applied in many fields. With the development of related technology, the application of different algorithms has improved the face recognition technology in different situations. This paper mainly reviews three mathematical models of face recognition based on neural networks, namely genetic algorithm and artificial neural network (GA-ANN), principal component analysis method and feed-forward neural networks (PCA-FNN) and Hidden Markov Models (HMMs) and State-Action-Reward-State-Action (SARSA). In this paper, three models are introduced and analysed in detail. Through analysis, it has been concluded that the GA-ANN improves classification accuracy, the PCA-FNN is used in face recognition with constant posture and the use of HMM-SARSA improves the accuracy of face recognition.

Security-oriented Face Detection Technology Utilizing Deep Learning Techniques Along with the CASIA Datasets

Recently, face recognition technology has become increasingly important for safety purposes. Masks are now required in most countries and are increasingly used. Public health professionals advise people to conceal their facial features outdoors to reduce COVID-19 transmission by 65%. Detecting people without masks on their faces is crucial. This has become widely used as face recognition outperforms PINs, passwords, fingerprints, and other safety verification methods. Sunglasses, scarves, caps, and makeup have made facial identification harder in recent decades. Thus, such masks impact facial recognition performance. Face masks also make traditional technology for facial recognition ineffective for face authorization, security checks, school monitoring, and cellphone and laptop opening. Thus, we proposed Masked Facial Recognition (MFR) to recognize veiled and exposed-face people so they don't need to remove their masks to verify themselves. This deep computing model was trained with Inception Res Network V1. CASIA is responsible for preparing pictures and using LFW to validate models. Dlib creates masked datasets utilizing vision algorithms. About 96% accuracy was achieved using our three models that were trained. Thus, covered and uncovered recognition of faces and detection techniques in security and safety verification might easily be used. These systems can be used in various settings, such as airports, train stations, and other public places, to enhance security and prevent crime. Overall, deep learning within face recognition technology has significant potential for improving safety and security in various settings.

Recovery-Based Occluded Face Recognition by Identity-Guided Inpainting.

Occlusion in facial photos poses a significant challenge for machine detection and recognition. Consequently, occluded face recognition for camera-captured images has emerged as a prominent and widely discussed topic in computer vision. The present standard face recognition methods have achieved remarkable performance in unoccluded face recognition but performed poorly when directly applied to occluded face datasets. The main reason lies in the absence of identity cues caused by occlusions. Therefore, a direct idea of recovering the occluded areas through an inpainting model has been proposed. However, existing inpainting models based on an encoder-decoder structure are limited in preserving inherent identity information. To solve the problem, we propose ID-Inpainter, an identity-guided face inpainting model, which preserves the identity information to the greatest extent through a more accurate identity sampling strategy and a GAN-like fusing network. We conduct recognition experiments on the occluded face photographs from the LFW, CFP-FP, and AgeDB-30 datasets, and the results indicate that our method achieves state-of-the-art performance in identity-preserving inpainting, and dramatically improves the accuracy of normal recognizers in occluded face recognition.