Face Recognition Task Research Articles

Enhancing Facial Recognition: A Comprehensive Review of Deep Learning Approaches and Future Perspectives

This paper presents a comprehensive review of the application of deep learning in facial recognition, covering fundamental aspects from neural network architectures to advanced training methods. It starts with an introduction to the basic architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), including variations like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. The review extends to sophisticated architectures like AlexNet, GoogleNet, VGGNet, and ResNet, which have significantly pushed the boundaries of accuracy in face recognition tasks. The paper details the process of data preprocessing in face recognition, which involves critical steps such as face detection, alignment, and normalization to ensure uniformity in the input data, enhancing the accuracy of feature extraction. Various feature extraction methods are discussed, including CNN-based and Generative Adversarial Network (GAN)-based techniques, which have shown considerable promise in deriving complex facial features from raw images. Loss functions such as Euclidean distance loss, angular/cosine margin loss, and softmax loss variants are explored to understand their impact on enhancing the discriminative power of the facial recognition systems. The paper highlights the evolution from traditional models using eigenfaces and feature descriptors like Local Binary Patterns (LBP) to cutting-edge deep learning models that utilize deep identity features (DeepID) and triplet loss functions to improve recognition accuracy.

HifiDiff: High-fidelity diffusion model for face hallucination from tiny non-frontal faces

Obtaining a high-quality frontal facial image from a low-resolution (LR) non-frontal facial image is crucial for many facial analysis tasks. Recently, diffusion models (DMs) have made impressive progress in near-frontal face super-resolution. However, when faced with non-frontal LR faces, the existing DMs exhibit poor identity preservation and facial detail fidelity. In this paper, we present a novel high-fidelity DM named HifiDiff for simultaneously super-resolving and frontalizing tiny non-frontal facial images. It consists of a two-stage pipeline: facial preview and facial refinement. In the first stage, we pretrain a coarse restoration module to obtain a coarse high-resolution (HR) frontal face, which serves as a superior constraint condition to enhance the ability to solve complex inverse transform issues. In the second stage, we leverage the strong generation capabilities of the latent DM to refine the facial details. Specifically, we design a two-pathway control structure that consists of a facial prior guidance (FPG) module and an identity consistency (IDC) module to control the denoising process. FPG encodes multilevel features derived from latent coarse HR frontal faces and employs hybrid cross-attention to capture their intrinsic correlations with the denoiser features, thereby improving the fidelity of the facial details. IDC utilizes contrastive learning to extract high-level semantic identity-representing features to constrain the denoiser, thereby maintaining the fidelity of facial identities. Extensive experiments demonstrate that our HifiDiff produces both high-fidelity and realistic HR frontal facial images, surpassing other state-of-the-art methods in qualitative and quantitative analyses, as well as in downstream facial recognition tasks.

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.

Low-resolution face recognition: Review, challenges and research directions

Low-resolution face recognition (LRFR) is an active research area as it is widely used in forensics and surveillance systems. A lot of effort has been put into improving the performance of the system since its inception. Recent deep neural network models have demonstrated outstanding face recognition performance on various face data sets with challenges like variations in pose, illumination, and occlusion and surpassed the performance of humans in these tasks. But, the accuracy of the LRFR method is still a problem. There is no fixed definition for considering any image as a low-resolution (LR) image. Most of the researchers have considered the image below 32 × 32 as a low-resolution image. This paper discusses various methods and algorithms used in improving the performance of low-resolution face recognition (LRFR). We have presented a thorough study of all the processes included in face recognition tasks including face detection, feature mapping, super-resolution, and face recognition. The study includes methodology along with the dataset and performance measures. We have also summarized the study of different datasets used in LRFR along with various source codes used to perform experimentation on LRFR. We have also presented a study in terms of accuracy of different LRFR methods on different dataset. Finally, challenges and research directions are presented to further carry out the LRFR research.

Eye movement characteristics of emotional face recognizing task in patients with mild to moderate depression.

Depression is a complex affective disorder characterized by high prevalence and severe impact, commonly presenting with cognitive impairment. The objective diagnosis of depression lacks precise standards. This study investigates eye movement characteristics during emotional face recognition task (EFRT) in depressive patients to provide empirical support for objective diagnosis. We recruited 43 patients with depression (Depressive patients, DP) from a psychiatric hospital and 44 healthy participants (Healthy Control, HC) online. All participants completed an EFRT comprising 120 trials. Each trial presented a gray screen for 800 ms followed by a stimulus image for judgment. Emotions were categorized as positive, neutral, or negative. Eye movement trajectories were recorded throughout the task. Latency of First Fixation (LFF), Latency of First Fixation for Eye AOI, and Latency of First Fixation for Mouth AOI were used as representative indicators of early attention, Proportion of Eye AOI, and Proportion of Mouth AOI as measures of intermediate attention, Accuracy (ACC) and Reaction Time (RT) as behavioral indicators of late-stage attention. In this study, these metrics were employed to explore the differences between patients with depression and healthy individuals. Compared to healthy participants, individuals with depression exhibit longer first fixation latencies on the eyes and mouth during the early attention stage of emotional face recognition, indicating an avoidance tendency toward key facial recognition cues. In the mid-to-late attention stages, depressive individuals show an increased fixation ratio on the eyes and a decreased fixation ratio on the mouth, along with lower accuracy and longer response times. These findings suggest that, relative to healthy individuals, individuals with depression have deficits in facial recognition. This study identified distinct attention patterns and cognitive deficits in emotional face recognition among individuals with depression compared to healthy individuals, providing an attention-based approach for exploring potential clinical diagnostic markers for depression.

Class‐wised domain decoupling‐guided adversarial feature learning for cross‐age face recognition

AbstractHow to extract age‐independent identity features from face images has long been the major challenge in cross‐age face recognition task. In this letter, a class‐wised domain decoupling‐guided adversarial feature learning network to extract age‐independent face features is proposed. In this model, an adversarial feature learning module is designed to decompose identity features and age features. Considering that artificially designed age interference suppression strategies are difficult to fit the complex relationship between identity features and age features, an adversarial training strategy incorporated with an attention mechanism in the adversarial feature learning module is introduced to suppress age interference adaptively. Besides, under the idea of maximizing the distribution difference of identity feature domain and age feature domain, a class‐wised domain decoupling module is further designed to guide the model to extract age‐independent identity features. The proposed model has been validated on the well‐known public datasets CALFW and CACD‐VS, where it achieved remarkable recognition accuracy rates of 94.5% and 99.5%, respectively. This represents improvements of 4.2% and 0.1% over the state‐of‐the‐art single‐task‐based models, clearly showcasing the effectiveness of the proposed class‐wised domain decoupling‐guided adversarial feature learning model.

Comprehensive comparison between vision transformers and convolutional neural networks for face recognition tasks

This paper presents a comprehensive comparison between Vision Transformers and Convolutional Neural Networks for face recognition related tasks, including extensive experiments on the tasks of face identification and verification. Our study focuses on six state-of-the-art models: EfficientNet, Inception, MobileNet, ResNet, VGG, and Vision Transformers. Our evaluation of these models is based on five diverse datasets: Labeled Faces in the Wild, Real World Occluded Faces, Surveillance Cameras Face, UPM-GTI-Face, and VGG Face 2. These datasets present unique challenges regarding people diversity, distance from the camera, and face occlusions such as those produced by masks and glasses. Our contribution to the field includes a deep analysis of the experimental results, including a thorough examination of the training and evaluation process, as well as the software and hardware configurations used. Our results show that Vision Transformers outperform Convolutional Neural Networks in terms of accuracy and robustness against distance and occlusions for face recognition related tasks, while also presenting a smaller memory footprint and an impressive inference speed, rivaling even the fastest Convolutional Neural Networks. In conclusion, our study provides valuable insights into the performance of Vision Transformers for face recognition related tasks and highlights the potential of these models as a more efficient solution than Convolutional Neural Networks.

Scene effects on disgusted facial expression detection in individuals with social anxiety: The role of emotional intensity

Individuals exhibiting high social anxiety (HSA) typically encounter challenges in identifying threatening stimuli with varying levels of intensity in different social scenes, ultimately affecting their social interactions. However, it is not well understood how social scenes, emotional intensity, and interaction influence the recognition of threat stimuli among HSA individuals (HSAs). To address this issue, a face recognition task was administered to 20 HSA participants and 22 individuals exhibiting low social anxiety (LSA) in this study. Results indicated that during the social scene presentation stage, HSAs produced larger P2 amplitude than LSA individuals (LSAs) no matter the valence of the scenes. During the face recognition stage, HSAs had smaller N170 amplitude than LSAs and exhibited lower recognition time for 2 % disgusted faces compared to LSAs. Furthermore, the consistency between scenes and faces led to faster recognition of disgusted faces in HSAs, but not in LSAs. Consequently, our findings suggested that HSAs exhibited unique cognitive processing patterns in social scenes, manifested by increased attention to scenes and decreased attention to faces. In addition, the emotional congruence between the scene and the faces could facilitate the recognition of faces by HSAs.

Face recognition using deep learning on Raspberry Pi

Abstract Facial recognition on resource-limited devices such as the Raspberry Pi poses a challenge due to inherent processing limitations. For real-time applications, finding efficient and reliable solutions is critical. This study investigated the feasibility of using transfer learning for facial recognition tasks on the Raspberry Pi and evaluated transfer learning that leverages knowledge from previously trained models. We compared two well-known deep learning (DL) architectures, InceptionV3 and MobileNetV2, adapted to face recognition datasets. MobileNetV2 outperformed InceptionV3, achieving a training accuracy of 98.20% and an F1 score of 98%, compared to InceptionV3’s training accuracy of 86.80% and an F1 score of 91%. As a result, MobileNetV2 emerges as a more powerful architecture for facial recognition tasks on the Raspberry Pi when integrated with transfer learning. These results point to a promising direction for deploying efficient DL applications on edge devices, reducing latency, and enabling real-time processing.

Enhancing the Transferability of Adversarial Patch via Alternating Minimization

Adversarial patches, a type of adversarial example, pose serious security threats to deep neural networks (DNNs) by inducing erroneous outputs. Existing gradient stabilization methods aim to stabilize the optimization direction of adversarial examples through accumulating gradient momentum to enhance attack transferability on black-box models. However, they are not fully effective for adversarial patches. The accumulated momentum during optimization often misaligns with the optimization direction, reducing their efficacy in black-box scenarios. We introduce an optimization method called Alternating Minimization for Adversarial Patch (AMAP). This method decomposes the original AP into multiple sub-patches and utilizes their update direction to stabilize the optimization of the original AP. Additionally, we propose an adaptive step size optimization method that accelerates convergence and boosts the attack performance. In face recognition tasks, AMAP outperforms baseline methods by a remarkable 5.21% and exceeds the second-best method by 1.4%. Furthermore, AMAP demonstrates practical feasibility in the physical domain, highlighting its potential for robust computer security testing applications.

Exploring Convolutional Neural Networks: A Study and Investigation Based on Face Recognition

Machine learning (ML) is profoundly transforming decision-making processes across a range of industries, simultaneously presenting challenges to existing labor markets and facilitating significant technological and innovative advancements. This paper undertakes a thorough exploration of the broad societal implications of ML and its pivotal contributions towards achieving sustainable development goals. It provides an in-depth examination of the foundational theories underpinning machine learning and presents a detailed taxonomy of ML algorithms, with a particular emphasis on the roles of convolutional and pooling layers in deep learning models utilized for image recognition tasks. Through rigorous evaluative analyses of various models, this study offers valuable insights into the comparative efficacy of these models. Specifically, the FaceRecognition model is highlighted for its superior accuracy, reduced latency, and enhanced throughput in face recognition tasks, outperforming established benchmarks. However, the study also identifies a notable deficiency in the model’s capability for object classification. Recommendations are made for reducing the complexity of models while simultaneously increasing their diversity, aiming to enhance overall performance. Furthermore, the paper underscores the significant impact these technologies on societal decision-making processes, emphasizing the need for careful consideration of both the potential benefits and the ethical implications associated with machine learning deployment.

Multi-model fusion of DSFD and Zero-DCE for low-light face detection

Due to the wide application of face detection, face recognition task under extreme lighting conditions inevitably becomes one of the challenges. Under low-light conditions, the face detection task becomes more complex and difficult due to the presence of more noise and lower visibility in the image. For this extreme environment, this paper proposes a multi-model-assisted solution that combines image enhancement and face detection models to handle this task. The DarkFace dataset was used in this study's validation of a number of distinct face identification models and picture enhancement techniques. The experimental findings demonstrate that, on this dataset, the DSFD (Dual Shot Face Detector) and Zero-DCE (Zero-Reference Deep Curve Estimation) methods perform noticeably better than the other techniques, achieving a recall of 24.61%. Therefore, these two algorithms are selected as the core processing flow in this paper to improve the facial detection system's low-light performance. This combination offers a workable answer to the problem of face detection in low light by effectively increasing the visibility of the picture and raising the accuracy of face detection.

FORMICARY SWARM OPTIMIZED DEEP CNN FOR FACIAL EMOTION RECOGNITION FROM HUMAN FACIAL EXPRESSIONS

Facial emotion recognition (FER) is a dominant research area that captures the biological facial features and matches the facial data with existing databases to analyze the individual’s emotional state. Numerous techniques have been formulated for attaining effective FER. However, the occlusions, different head positions, deformed faces, motion blur under unrestricted settings, and complicated backgrounds make it complex to analyze the facial images. In this paper, the formicary swarm optimization-based deep convolutional neural network (FSO-opt DCNN) model is utilized for facial emotion detection for which JAFFE and the RAVDESS facial expression datasets are used. DCNNs are proficient with built-in feature extraction strategies from images to map the various facial expressions to the corresponding emotional states adopted for effective FER. In addition, the intensity, directional, and edge patterns as well as the correlation features extracted utilizing the hybrid textual pattern, RESNET 101 and VGG 16-based correlation modules assist the DCNN to attain the informative feature from the high-resolution images. Further, the formicary swarm optimization (FSO) is incorporated that effectively tunes the DCNN to capture the complex relationships between the learned features that excel the FER capability. Evaluating the metrics, the face recognition model using the RAVDESS dataset achieves notable efficiencies during a training percentage (TP) of 90%, with values of 97.51%, 95.48%, 99.55%, 95.48%, 97.48%, 96.47%, and a minimum loss of 2.49%. Simultaneously, the emotion detection model demonstrates robust efficiencies with metric values of 96.75%, 98.49%, 95.01%, 98.49%, 96.72%, 97.59%, and a minimum loss of 3.25%. Finally, the obtained results reveal the efficacy of the FSO-opt DCNN, particularly in face recognition tasks, as it outperforms the existing models across various datasets, showcasing its versatility and potential in facial analysis applications.

Perbandingan Dataset Labelled Faces in the Wild (LFW) dan faces94 Menggunakan Algoritma Convolutional Neural Networks (CNN) untuk Pengenalan Wajah

This research compares the performance of two popular datasets, Labelled Faces in the Wild (LFW) and faces94, in the task of face recognition using Convolutional Neural Networks (CNN) algorithms. The LFW dataset is known for its high variation in pose, lighting, and expression, while faces94 is more structured with more uniform lighting and pose conditions. CNNs were chosen for their ability to extract important features from face images for classification. In this study, a CNN model was trained on both datasets and its performance was evaluated using accuracy, precision, and recall metrics. The experimental results showed that the model trained on the faces94 dataset achieved higher accuracy compared to the model trained on the LFW dataset. However, the model on the LFW dataset demonstrated better resilience to variations in lighting and pose conditions. These findings indicate that while a more structured dataset like faces94 can produce a model with high accuracy under testing conditions similar to the training data, a dataset with greater variation like LFW is more suitable for real-world applications involving diverse conditions. This study provides important insights into the selection of datasets for developing robust and accurate face recognition systems.

Kernel Angle Dependence Measures in Metric Spaces

Measuring and testing dependence between data in separable metric spaces is of great importance in modern statistics. Most existing work relied on the distance between random variables, which inevitably required the moment conditions to guarantee the distance is well-defined. Based on the geometry element “angle”, we develop a novel class of nonlinear dependence measures for data in metric space that can avoid such conditions. Specifically, by making use of the reproducing kernel Hilbert space equipped with Gaussian measure, we introduce kernel angle covariances that can be applied to various types of data, including low dimensional vector, high dimensional vectors, non-Euclidean data like symmetric positive definite matrices, and compositional data. We estimate kernel angle covariances based on U-statistic and establish the corresponding independence tests via gamma approximation. Our kernel angle independence tests, imposing no-moment conditions on kernels, are robust with heavy-tailed random variables. We conduct comprehensive simulation studies and apply our proposed methods to a facial recognition task. Our kernel angle covariances-based tests show remarkable performances in dealing with image data. All the codes and proofs are included in the supplementary materials.

Face recognition's practical relevance: Social bonds, not social butterflies

Research on individual differences in face recognition has provided important foundational insights: their broad range, cognitive specificity, strong heritability, and resilience to change. Elusive, however, has been the key issue of practical relevance: do these individual differences correlate with aspects of life that go beyond the recognition of faces, per se? Though often assumed, especially in social realms, such correlates remain largely theoretical, without empirical support. Here, we investigate an array of potential social correlates of face recognition. We establish social relationship quality as a reproducible correlate. This link generalises across face recognition tasks and across independent samples. In contrast, we detect no robust association with the sheer quantity of social connections, whether measured directly via number of social contacts or indirectly via extraversion-related personality indices. These findings document the existence of a key social correlate of face recognition and provide some of the first evidence to support its practical relevance. At the same time, they challenge the naive assumption that face recognition relates equally to all social outcomes. In contrast, they suggest a focused link of face recognition to the quality, not quantity, of one's social connections.

Integration of Manifold Learning and Density Estimation for Fine-Tuned Face Recognition

With the rapid advancements in data analysis and the increasing complexity of high-dimensional datasets, traditional dimensionality reduction techniques like Local Linear Embedding (LLE) often face challenges in maintaining accuracy and efficiency. This research aims to overcome the limitations of LLE, specifically its reliance on the nearest neighbor concept, its inability to distinguish differences among manifold points, and its underutilization of data discrimination information. To address these issues, we propose an advanced LLE algorithm that integrates decision tree-based neighbor recognition with Gaussian kernel density estimation. Decision trees accurately determine neighboring relationships, which are then optimized using Gaussian kernel density estimation to better reflect the distribution of sample points on the manifold. The algorithm also incorporates data discrimination information to enhance classification accuracy and efficiency. Evaluations in facial recognition tasks using SVM classifiers demonstrate significant improvements. Integrating decision trees (LLE-DT) yielded accuracy gains, with LFW at 98.75%, CFP 96.10%, and Olivetti 92.18%. Gaussian density estimation (LLE-GDE) achieved further enhancements, especially in LFW (99.13%), with CFP at 96.85%, and Olivetti at 91.82%. Combining both methods (LLE-DT-GDE) led to substantial improvements: LFW 99.61%, CFP 97.23%, and Olivetti 93.56%, highlighting the synergy between decision trees and Gaussian estimation. This advanced LLE algorithm effectively addresses the limitations of traditional approaches, showing promising results in complex data processing tasks such as facial recognition. These findings suggest its potential for broader applications in fields requiring robust data analysis and classification.

Convolutional neural networks to classify human stress that occurs during in‐field sugarcane harvesting: A case study

AbstractAssessing human stress in agriculture proves to be a complex and time‐intensive endeavor within the field of ergonomics, particularly for the development of agricultural systems. This methodology involves the utilization of instrumentation and the establishment of a dedicated laboratory setup. The complexity arises from the need to capture and analyze various physiological and psychological indicators, such as heart rate (HR), muscle activity, and subjective feedback to comprehensively assess the impact of farm operations on subjects. The instrumentation typically includes wearable devices, sensors, and monitoring equipment to gather real‐time data of subject during the performance of farm operations. Deep learning (DL) models currently achieve human performance levels on real‐world face recognition tasks. In this study, we went beyond face recognition and experimented with the recognition of human stress based on facial features during the drudgery‐prone agricultural operation of sugarcane harvesting. This is the first research study for deploying artificial intelligence‐driven DL techniques to identify human stress in agriculture instead of monitoring several ergonomic characteristics. A total of 20 (10 each for male and female) subjects comprising 4300 augmented RGB images (215 per subject) were acquired during sugarcane harvesting seasons and then these images were deployed for training (80%) and validation (20%). Human stress and nonstress states were determined based on four ergonomic physiological parameters: heart rate (ΔHR), oxygen consumption rate (OCR), energy expenditure rate (EER), and acceptable workload (AWL). Stress was defined when ΔHR, OCR, EER, and AWL reached or exceeded certain standard threshold values. Four convolutional neural network‐based DL models (1) DarkNet53, (2) InceptionV3, (3) MobileNetV2 and (4) ResNet50 were selected due to their remarkable feature extraction abilities, simple and effective implementation to edge computation devices. In all four DL models, training performance results delivered training accuracy ranging from 73.8% to 99.1% at combinations of two mini‐batch sizes and four levels of epochs. The maximum training accuracies were 99.1%, 99.0%, 97.7%, and 95.4% at the combination of mini‐batch size 16 and 25 epochs for DarkNet53, InceptionV3, ResNet50, and MobileNetV2, respectively. Due to the best performance, DarkNet53 was tested further on an independent data set of 100 images and found 89.8%–93.3% confident to classify stressed images for female subjects while 92.2%–94.5% for male subjects, though it was trained on the integrated data set. The comparative classification of the developed model and ergonomic measurements for stress classification was carried out with a net accuracy of 88% where there were few instances of wrong classifications.

Residual serialized cross grouping transformer for small scale sketch face recognition

Sketch face recognition has recently gained significant attention in the field of computer vision due to its ability to quickly identify matched pairs of optical and sketch images. This technology has the potential to greatly improve the efficiency of law enforcement agencies in criminal investigations. However, there are still challenges that need to be addressed in sketch face recognition algorithms, such as modal differences and limited sample sizes. To overcome these issues, this study proposes a Residual Serialized Cross Grouping Transformer (RSCGT), which contains a residual serialized module to reduce the computation complexity, a two-layer Cross Grouping Transformer module that is capable of extracting modality-invariant context features, a domain adaptive module to mitigate the impact of modal differences. Additionally, we introduce a meta-learning training strategy to augment the generalization ability of this model. Experimental results demonstrate that the RSCGT achieves high accuracy in sketch face recognition tasks, even with small-scale datasets.

“Sparse + Low-Rank” tensor completion approach for recovering images and videos

Recovering color images and videos from highly undersampled data is a fundamental and challenging task in face recognition and computer vision. By the multi-dimensional nature of color images and videos, in this paper, we propose a novel tensor completion approach, which is able to efficiently explore the sparsity of tensor data under the discrete cosine transform (DCT). Specifically, we introduce two “sparse + low-rank” tensor completion models as well as two implementable algorithms for finding their solutions. The first one is a DCT-based sparse plus weighted nuclear norm induced low-rank minimization model. The second one is a DCT-based sparse plus p-shrinking mapping induced low-rank optimization model. Moreover, we accordingly propose two implementable augmented Lagrangian-based algorithms for solving the underlying optimization models. A series of numerical experiments including color image inpainting and video data recovery demonstrate that our proposed approach performs better than many existing state-of-the-art tensor completion methods, especially for the case when the ratio of missing data is high.