Face Recognition Task Research Articles

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.

Degradation model and attention guided distillation approach for low resolution face recognition

Deep convolution neural networks (CNN) have shown their efficacy in face recognition tasks due to their ability to extract highly discriminant face representations from face images. On high-resolution benchmark datasets, outstanding identification and verification results have been achieved. However, the performance of these algorithms is significantly degraded when tested on low-resolution (LR) images such as those captured from surveillance cameras. A straightforward solution to this problem is to use both high-resolution (HR) images and corresponding down-sampled LR images during training. Although this strategy improves the performance for LR images, it has some limitations. First, there is a significant difference between down-sampled LR images and LR images from surveillance cameras, leading to performance saturation at an earlier stage. Another limitation is the deterioration in the performance of HR images. In this work, solutions to both these limitations are proposed. A degradation model is proposed that synthesizes LR images from corresponding HR, emulating the real-world degradation effects in synthetic data, thus enabling the face recognition system to tolerate various blurry and noisy effects. To address the deterioration in the performance of HR images, an attention-guided distillation is proposed, which utilizes attention maps from convolutional layers in combination with deep features to transfer informative HR features from teacher to student network. The attention maps from the teacher network guide the student network to a better optimum and produce resolution robust face representations. The results of the proposed approach on the popular LR datasets like SCface, Coxface, and PaSC show that it outperforms the recent state-of-the-art (SOTA) techniques by a significant margin demonstrating its effectiveness for different cross-resolution scenarios.

Application of Multi-sensory interaction design Based on Machine Learning in Virtual Reality

Virtual reality (VR) technology is well-known to the public for its unparalleled sense of environmental immersion, which is accompanied by a large amount of human-computer interaction between users and objects in the virtual environment. Users transmit and obtain information through their senses, so the processing mechanism of information in various senses under the background of VR technology has become the foundation of research. In the process of natural information dissemination, users’ senses are synergistically involved, so whether there is a correlation between various senses that affects users’ final perception has become an important theoretical support for studying multi-sensory interaction. Facial expression recognition is an important component of multi-sensory interaction technology. Therefore, this article mainly uses machine learning algorithms to achieve real-time detection of facial targets in images or videos, as well as classification and recognition of facial expressions. To meet the task requirements of the expression recognition module, this article selects the DeepID network model based on deep learning in the field of facial recognition as the basic model for the expression recognition function. This study utilizes the excellent feature extraction ability of the original network for facial images and introduces residual modules and attention mechanisms, making the modified network model more suitable for facial expression multi classification recognition tasks. The recognition accuracy of the facial expression classification algorithm proposed by this work reaches 97.2%.

Cyclic style generative adversarial network for near infrared and visible light face recognition

Near Infrared and Visible Light (NIR-VIS) face recognition attracts attention from researchers because of its potential for safety, illumination invariance, and stability. Nevertheless, the difference between NIR and VIS domains, the domain gap, remains a huge problem for matching NIR and VIS images. Specifically, for the same identity, the appearance in the NIR domain is different from the VIS domain. Thus, traditional face recognition methods cannot be effective. To address this problem, this paper proposes a novel model, called Cyclic-Style GAN (CS-GAN). First, the pre-trained Style-GAN 3 network is embedded into the Cycle-GAN structure for NIR-VIS cross-domain learning. Second, there is a cyclic subspace learning method consisting of latent loss and style loss, through which both style (domain feature) and facial characteristic features are learned to improve the quality of synthesized images. The model synthesizes realistic VIS images from NIR images and does the face recognition task in the VIS domain. The proposed method achieves 99.6% Rank-1 accuracy on the CASIA NIR-VIS 2.0 database which is a state-of-the-art result. The visualization results show that the proposed model synthesizes VIS images with a clear texture of faces and in close-to-reality color.

Emotion regulation effects of Ayahuasca in experienced subjects during implicit aversive stimulation: An fMRI study

Ethnopharmacological relevanceAyahuasca is a beverage used in Amazonian traditional medicine and it has been part of the human experience for millennia as well as other different psychoactive plants. Although Ayahuasca has been proposed as potentially therapeutic as an anxiolytic and antidepressant, whilst no studies have been carried out so far investigating their direct effect on brain emotional processing. Aim of the studyThis study aimed to measure the emotional acute effect of Ayahuasca on brain response to implicit aversive stimulation using a face recognition task in functional magnetic resonance imaging (fMRI). Materials and methodsNineteen male experienced Ayahuasca users participated in this study in two fMRI sessions before and after 50 min of the Ayahuasca ingestion. Subjects were presented with pictures of neutral (A) and aversive (B) (fearful or disgusted) faces from the Pictures of Facial Affect Series. Subjects were instructed to identify the gender of the faces (gender discrimination task) while the emotional content was implicit. Subjective mood states were also evaluated before Ayahuasca intake and after the second fMRI session, using a visual analogue mood scale (VAMS). ResultsDuring the aversive stimuli, the activity in the bilateral amygdala was attenuated by Ayahuasca (qFDR<0.05). Furthermore, in an exploratory analysis of the effects after intake, Ayahuasca enhances the activation in the insular cortex bilaterally, as well as in the right dorsolateral prefrontal cortex (qFDR<0.05). In the psychometric VAMS scale, subjects reported attenuation of both anxiety and mental sedation (p < 0.01) during acute effects. ConclusionsTogether, all reported results including neuroimaging, behavioral data and psychometric self-report suggest that Ayahuasca can promote an emotion regulation mechanism in response to aversive stimuli with corresponding improved cognition including reduced anxiety and mental sedation.

Self-supervised Learning for Expression Recognition on Small-scale Data Set

In this paper, we propose a facial micro-expression recognition method utilizing self-supervised learning and Vision Transformer. We employ a contrast learning approach for self-supervision and extract image features using an attention mechanism. Various data augmentation techniques were utilized, and we specifically designed an enhancement method for facial recognition. By combining the strengths of the Vision Transformer and CNN models for feature extraction, our approach achieves improved recognition accuracy, even with limited labeled data. Experimental evaluation shows that our proposed method has good results in facial micro-expression recognition tasks.

Can machine recognize a long-missed old friend? A test to the FaceNet face recognition algorithm

Facial recognition receives heated discussion in recent years. Given the increasing demand for portable and easy-to-use face recognition programs, several coding libraries have been developed that includes the algorithm for identifying and classifying faces. One example of such libraries, the “face_recognition” library developed by Adam Geitgey allows fast execution for face recognition tasks and requires only 1 photo as training input. It adopts the Convolutional Neural Network (CNN) from OpenFace, which is built based on the FaceNet algorithm. Although the study claims that the algorithm has a high base on tests from common face dataset, it is not clear whether the algorithm can identify people consistently when they are in their different ages. In this paper, a face dataset is constructed by collecting publicly available photos from celebrities that can be traced to a certain date of taken. These photos are grouped by 5 pieces per person with each photo taken roughly 4 years apart from the previous one. Then, experiments are conducted to test the effectiveness of face_recognition library on identifying the person from these groups of photos. The results report an over 75% success rate for the algorithm on image identification tasks, indicating a good ability for the face encoding to resist the gap of time.

Neurophysiological responses to emotional faces predict dynamic fluctuations in affect in adolescents.

The ability to accurately identify and interpret others' emotions is critical for social and emotional functioning during adolescence. Indeed, previous research has identified that laboratory-based indices of facial emotion recognition and engagement with emotional faces predict adolescent mood states. Whether socioemotional information processing relates to real-world affective dynamics using an ecologically sensitive approach, however, has rarely been assessed. In the present study, adolescents (N = 62; ages 13-18) completed a Facial Recognition Task, including happy, angry, and sad stimuli, while EEG data were acquired. Participants also provided ecological momentary assessment (EMA) data probing their current level of happiness, anger, and sadness for 1-week, resulting in indices of emotion (mean-level, inertia, instability). Analyses focused on relations between (1) accuracy for and (2) prolonged engagement with (LPP) emotional faces and EMA-reported emotions. Greater prolonged engagement with happy faces was related to less resistance to changes in happiness (i.e., less happiness inertia), whereas greater prolonged engagement with angry faces associated with more resistance to changes in anger (i.e., greater anger inertia). Results suggest that socioemotional processes captured by laboratory measures have real-world implications for adolescent affective states and highlight potentially actionable targets for novel treatment approaches (e.g., just-in-time interventions). Future studies should continue to assess relations among socioemotional informational processes and dynamic fluctuations in adolescent affective states.

An attentional residual feature fusion mechanism for sheep face recognition

In the era of globalization and digitization of livestock markets, sheep are considered an essential source of food production worldwide. However, sheep behavior monitoring, disease prevention, and precise management pose urgent challenges in the development of smart ranches. To address these problems, individual identification of sheep has become an increasingly viable solution. Despite the benefits of traditional sheep individual identification methods, such as accurate tracking and record-keeping, they are labor-intensive and inefficient. Popular convolutional neural networks (CNNs) are unable to extract features for specific problems, further complicating the issue. To overcome these limitations, an Attention Residual Module (ARM) is proposed to aggregate the feature mapping between different layers of the CNN. This approach enables the general model of the CNN to be more adaptable to task-specific feature extraction. Additionally, a targeted sheep face recognition dataset containing 4490 images of 38 individual sheep has been constructed. Furthermore, the experimental data was expanded using image enhancement techniques such as rotation and panning. The results of the experiments indicate that the accuracy of the VGG16, GoogLeNet, and ResNet50 networks with the ARM improved by 10.2%, 6.65%, and 4.38%, respectively, compared to these recognition networks without the ARM. Therefore, the proposed method for specific sheep face recognition tasks has been proven effective.

Neutral Face Learning and Progressive Fusion Synthesis Network for NIR-VIS Face Recognition

To meet the strong demand for deploying face recognition systems in low-light scenarios, the Near-InfraRed and VISible (NIR-VIS) face recognition task is receiving increasing attention. However, heterogeneous faces have the characteristics of heterogeneity and non-neutrality. Heterogeneity refers to the fact that the matching images are in different modalities, and non-neutrality means that the matching images are significantly different in pose, expression, lighting, etc. Both situations pose challenges for NIR-VIS face matching. To address this problem, we propose a novel Neutral face Learning and Progressive Fusion synthesis (NLPF) network to disentangle the latent attributes of heterogeneous faces and learn neutral face representations. Our approach naturally integrates Identity-related Neutral face Learning (INL) and Attribute Progressive Fusion (APF) into a joint framework. Firstly, INL eliminates modal variations and residual variations by guiding the network to learn homogeneous neutral face feature representations, which tackles the challenge of heterogeneity and non-neutrality by mapping cross-modal images to a common neutral representation subspace. Besides, APF is presented to perform the disentanglement and reintegration of identity-related features, modality-related features and residual features in a progressive fusion manner, which helps to further purify identity-related features. Comprehensive evaluations are carried out on three mainstream NIR-VIS datasets to verify the robustness and effectiveness of the NLPF model. In particular, NLPF has competitive recognition performance on LAMP-HQ, the most challenging NIR-VIS dataset so far.

The positive effects of symbolized characteristics of Luojing cross pattern decoration on public anxiety disorders based on social psychology

BackgroundIn recent years, the incidence of anxiety disorders has been increasing. The cause of anxiety disorders is not clear, but it may be related to genetic factors, personality characteristics, cognitive processes, etc. In order to study the positive impact of anxiety disorder among people in intangible cultural heritage, the social behavior of Luojing cross pattern symbolization and the emotional face of people with anxiety disorder were identified.Subjects and MethodsThe study used task-state fMRI technology, which required participants to complete the task of emotional face recognition during the MRI scan. The study compared the difference in activation of brain regions under positive and negative emotional faces between the GAD patient group and the healthy control group. SPSS24.0 was used for data analysis.ResultsIn positive mood, compared with healthy controls, the brain regions with significantly enhanced activation in the anxiety disorder group included the right posterior central gyrus (t=3.02, P&lt;0.05), the right superior temporal gyrus (t=3.86, P&lt;0.05), and the right caudate nucleus (t=4.02, P&lt;0.05). The brain regions with significantly weakened activation in the anxiety disorder group included the right superior frontal gyrus (t=3.11, P&lt;0.05), the right medial frontal lobe (t=4.01, P&lt;0.05), the left middle frontal gyrus (t=2.69, P&lt;0.05), the left insula (t=3.08, P&lt;0.05), the left anterior cingulate gyrus (t=2.38, P&lt;0.05), the left hippocampus (t=3.48, P&lt;0.05), etc.ConclusionsWhen the Luojing cross pattern ornament was symbolized, the mood of the people with anxiety disorders was more positive. This helps in the diagnosis and treatment of anxiety disorders from a social perspective.AcknowledgementA School-level Educational Teaching Research Project at Shanghai Business School in 2023 (No.SBS-2023-XJJG-07); Shanghai Cultural and Educational Integration project in 2023.

Sex Differences in Recognition of Face Expressions

Abstract: Emotional facial expressions are a crucial non-verbal communication skill for humans’ interactions. The current study assessed the impact of sex on emotional face recognition. A total of 125 individuals per-formed an online emotional face recognition task. The stimuli were created recutting male and female Saudi volunteers. Results showed that, except of the male participants having faster responses compared to females in general, no main significant differences between the sex group in accuracy nor any significant interaction between participant sex and the sex of the faces. Our findings suggests that the effect of sex on emotional face recognition needs further investigation with well calibrated stimuli. Limitations and the direction of future research in this area were discussed.

Implementation of Face Recognition for Attendance Recording in Online Learning

The utilization of facial recognition technology has become increasingly imperative within the realm of online learning. The current study introduces a novel system that utilizes face recognition technology to record attendance in online learning environments. The attendance system necessitates students to activate an attendance button, whereby their attendance is subsequently documented through facial recognition technology. The system recognizes students as present solely based on facial recognition. The system stores the duration of online learning activities in a database. Implementing machine learning methodologies, specifically face detection algorithms, improves precision and efficacy in administering student attendance in online education. The system utilizes Haar cascades in OpenCV to detect faces, extract features such as eyes, nose, and mouth, and classify them using LBPH. Through extensive experiments, an accuracy rate of 93.55% was achieved. The study demonstrates the effectiveness of the combined approach, showcasing the potential of Haar cascades and LBPH in face recognition tasks. The present study makes a valuable contribution to the domains of computer vision and educational technology by offering a pragmatic remedy for attendance tracking in virtual learning settings.

Elevated EEG heartbeat-evoked potentials in adolescents with more ADHD symptoms

IntroductionSymptoms of attention deficit hyperactivity disorder (ADHD) are associated with a variety of mental abnormalities, but little is known about the perception and processing of internal signals, i.e., interoception, in individuals with ADHD symptoms. This study aimed to investigate the association between ADHD symptoms and the heartbeat-evoked potential (HEP), known as a neural correlate of automatic interoceptive processing of cardiac signals, in adolescents. MethodsHEPs of 47 healthy adolescent participants (53.2 % female) with a mean age of 14.29 years were measured during an emotional face recognition task. In addition, participants completed a self-report screening for ADHD symptoms. ResultsADHD symptoms were positively related to the HEP activity during the task in three of eight EEG sectors in the left hemisphere, as well as in all sectors in the right hemisphere. DiscussionThis study is the first to demonstrate preliminary a relationship between the strength of HEP activity and ADHD symptoms in awake subjects. This finding of higher HEP amplitudes in subjects with more ADHD symptoms can be interpreted in terms of (i) increased arousal, (ii) altered neural processing of internal processes in an emotion-relevant task, and (iii) a misaligned precision-weighting process of task-irrelevant stimuli according to the predictive coding framework. These different interpretations could be reflected by previous studies showing heterogeneity of psychological deficits in individuals with ADHD symptoms. However, the generalizability to patients with diagnosed ADHD is limited due to the measurement tool for ADHD symptoms and the sample characteristics.

DAFL: Domain adaptation-based federated learning for privacy-preserving biometric recognition

With the introduction of data protection regulation in various countries, traditional centralized learning for the exploitation of sensitive biological information will gradually become unsustainable. We take face and speaker recognition systems as examples, to consider the possibility of implementing them using federated learning. It is known that federated learning methods still have some issues to be solved, e.g., parameter updates delivered can still reveal user privacy. Besides, existing privacy-preserving methods such as encryption and perturbation could pose other problems, including low accuracy and high training overhead, which can be more prominent in biometric recognition scenarios. We adopt the idea of domain adaptation and propose a new federated learning framework, DAFL, focusing on the improvement of the training efficiency and model performance, to achieve a practical privacy-preserving biometric recognition scheme for the first time. We also design a special dropout method for it to address the possible imbalance of biometric samples across users. Experiments on real-world datasets show that our approach can achieve up to 95.6% and 97.9% accuracy in face recognition and speaker recognition tasks, respectively, under medium privacy settings. Compared with existing work, it can save 56.0% of training time with a 3.1% accuracy loss in the best case.

Hypersphere guided embedding for masked face recognition

Hypersphere Guided Embedding for Masked Face Recognition has been proposed to address the problem encountered in the Masked Face Recognition task, which arises due to non-biological information from occlusions. While some existing algorithms prefer to digesting the existence of masks by probing and covering, others aim to integrate face recognition and masked face recognition tasks into a unified solution domain. In this paper, we propose a framework to enable existing methods to accommodate multiple data distributions by orthogonal subspaces. Specifically, We introduce constraints on multiple hypersphere manifolds via Multi-Center Loss and employ a Spatial Split Strategy to ensure the orthogonality of base vectors associated with different hypersphere manifolds, corresponding to distinct distribution. Our method is extensively evaluated on publicly available datasets on face recognition, mask face recognition and occlusion, demonstrating promising performance. Our code is available on an anonymous website: https://github.com/CaptainKai/HE_MFR.

Development of Deep Learning Algorithms for Improved Facial Recognition in Security Applications

This research aims to develop artificial intelligence (AI) algorithms in the context of facial recognition with a focus on increasing accuracy in difficult environmental conditions. Although facial recognition technology has made great progress, challenges such as poor lighting, variations in facial expressions, and head rotation are still problems that must be overcome. The research methodology involved collecting a wide dataset covering a wide variety of faces under various environmental conditions. This data is then processed and its features are extracted using computer image processing techniques. Furthermore, several deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were developed, trained, and evaluated for face recognition tasks. The expected result is the development of an AI algorithm that is able to overcome challenges in facial recognition with higher accuracy than existing methods. In particular, significant improvements in facial recognition accuracy are expected especially under low lighting conditions and variations in facial expressions. This research has a major impact in a variety of security applications, such as border surveillance, building access control, and corporate security. With higher facial recognition accuracy, security risks can be significantly reduced, resulting in safer and more efficient security solutions. In conclusion, this research aims to bring innovation in facial recognition technology through advanced AI approaches, with the potential to improve security in various contexts.

Multi-label recognition of facial body constitution via MLP-like deep networks with non-equilibrium weight loss

In multi-label image classification tasks, uncertainty in the number of sample labels is a common problem, which can lead to the existence of a unbalance in the labels of samples. Facial body constitution recognition is one of the important methods of traditional Chinese medicine (TCM) body constitution recognition, which has important significance for body constitution conditioning and disease prevention. Based on the sandwich constitution of the TCM body constitution theory, the multi-label constitution corresponding to the facial image is regarded as a multi-label recognition task. A new multi-label body constitution (MLBC) dataset is proposed using facial images in this paper. We propose a novel deep network via the MLP-like architectures, including Wave-MLP, Vip, and Cycle-MLP, for the multi-label facial body constitution recognition task with a non-equilibrium weight (NEBW) loss function. The experiments compare five commonly used multi-label classification loss functions for analyzing their recognition performance. The results show the effectiveness of the proposed NEBW loss function on this task. Notably, compared with the benchmark binary cross-entropy (BCE) loss function, the three kinds of MLP-like models improve by 1.50, 1.78, and 1.44 percentage points from Acc, respectively; the improvements are 1.09, 2.00, and 2.19 percentage points from mAP, respectively. And the improvements are 1.06, 2.43, and 0.77 percentage points from AUC, respectively.

Chronotype and Time of Day Effects on a Famous Face Recognition Task with Dynamic Stimuli.

Chronotype and Time of Day (ToD) can modulate several aspects of cognitive performance. However, there is limited evidence about the effect of these variables on face recognition performance, so the aim of the present study is to investigate this influence. For this, 274 participants (82.5% females; age 18-49 years old, mean = 27.2, SD = 1.82) were shown 20 short videoclips, each gradually morphing from a general identity unfamiliar face to a famous face. Participants should press the spacebar to stop each video as soon as they could identify the famous face, and then provide the name or an unequivocal description of the person. Analysis of response times (RT) showed that evening-types recognised the faces faster than morning-types. Considering different ToD windows, the effect of chronotype was only significant in the 13h-17h and in the 21h-6h time-windows. Altogether, results suggest an advantage of evening-types on famous face recognition using dynamic stimuli with morning-types, being particularly slower during their non-optimal period.

Investigating the role of the fusiform face area and occipital face area using multifocal transcranial direct current stimulation

The functional role of the occipital face area (OFA) and the fusiform face area (FFA) in face recognition is inconclusive to date. While some research has shown that the OFA and FFA are involved in early (i.e., featural processing) and late (i.e., holistic processing) stages of face recognition respectively, other research suggests that both regions are involved in both early and late stages of face recognition. Thus, the current study aims to further examine the role of the OFA and the FFA using multifocal transcranial direct current stimulation (tDCS). In Experiment 1, we used computer-generated faces. Thirty-five participants completed whole face and facial features (i.e., eyes, nose, mouth) recognition tasks after OFA and FFA stimulation in a within-subject design. No difference was found in recognition performance after either OFA or FFA stimulation. In Experiment 2 with 60 participants, we used real faces, provided stimulation following a between-subjects design and included a sham control group. Results showed that FFA stimulation led to enhanced efficiency of facial features recognition. Additionally, no effect of OFA stimulation was found for either facial feature or whole face recognition. These results suggest the involvement of FFA in the recognition of facial features.