Face Recognition Research Articles

BabyFM: Towards accurate 3D baby facial models using spectral decomposition and asymmetry swapping.

In this paper, we present the first publicly available 3D statistical facial shape model of babies, the Baby Face Model (BabyFM). Constructing a model of the facial geometry of babies entails specific challenges, such as occlusions, extreme and uncontrollable expressions, and data shortage. We address these challenges by proposing (1) a non-template dependent method that jointly estimates a 3D facial baby-specific template and the point-to-point correspondences; (2) a novel method to establish correspondences based on the spectral decomposition of the Laplace Beltrami Operator, which provides a more robust theoretical foundation than state-of-the-art methods; and (3) an asymmetry-swapping strategy to alleviate the shortage of large scale datasets by decoupling the identity-related and the asymmetry-related shape deformation fields. The latter leads to a data augmentation technique that we integrate within the Gaussian Process Morphable Model framework, providing a simple way of combining synthetic or sample covariance functions. We exhaustively evaluate each stage of our method and demonstrate that (1) when aiming at the 3D facial geometry of a baby, a specific model of babies is needed, since the pre-built publicly available models constructed with adults or older children are not able to accurately represent the facial shape of babies; (2) our spectral approach improves correspondences accuracy with respect to state-of-the-art-methods; and (3) the proposed data augmentation technique enhances the robustness of the BabyFM.

Enhancing social functioning using multi-user, immersive virtual reality

Rates of loneliness and other forms of social disconnection have been increasing worldwide. Prior studies have suggested that brief behavioral interventions can teach skills that may improve social functioning and connection but, currently, access to such interventions is limited. One previously untested approach for addressing this gap is to teach these skills using immersive, multi-user virtual reality (VR). To measure the feasibility of this approach, 33 young adults experiencing social discomfort were enrolled in a study of a VR-based application called Reconnecting with Ourselves and Others in virtual Meetings (ROOM), which delivered a previously validated, mindfulness-based intervention. Satisfaction ratings, qualitative feedback, and quantitative measures of aspects of social functioning, including social comfort, interpersonal distance, and facial affect recognition, were collected from participants. All of the participants attended more than half of the sessions and 90.3% found ROOM to be beneficial and useful. In addition, comfort with others in the real world (p = 0.02) and facial affect recognition accuracy (p = 0.02) significantly increased, while interpersonal distances remained unchanged, following ROOM. An immersive, multi-user VR intervention may represent one feasible, scalable approach for disseminating skills that can improve social functioning and connection. Trial registration Clinicaltrial.org # NCT06360562.

Deficits in facial emotion recognition and cognitive function among baby boomers.

Facial emotion recognition (FER), a key component of social cognition, plays a critical role in social interactions. In the aging process, FER among older adults holds significant potential as a tool for diagnosing cognitive function or enhancing interpersonal relationships. However, research in this area remains limited. This study aims to address this gap by examining the impact of cognitive function on FER among Korean baby boomers aged 60 to 69. Eighty-one participants completed the Korean version of the Montreal Cognitive Assessment (K-MOCA) and FER tasks. Of the participants, 69% had normal cognition, while 31% had mild cognitive impairment. Participants with normal cognition were 1.59 times more likely to recognize facial expressions correctly than those with impaired cognition (AOR=1.59, p<0.0001). They showed significantly higher odds of recognizing happy (AOR=9.68), anger (AOR=2.25), disgust (AOR=1.95), neutral (AOR=3.02), and surprise (AOR=2.27) expressions (p<0.001). Participants with normal cognition participants also scored higher on overall intensity for every emotion except sadness (p<0.001). Among the seven domains of the MoCA, three sub-domains-visuospatial/executive function, language, and orientation-showed significant associations with overall FER. These results highlight the diagnostic potential of FER tasks in identifying cognitive disorders and enhancing social skills in clinical and practical settings.

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

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

Interracial contact shapes racial bias in the learning of person-knowledge

During impression formation, perceptual cues facilitate social categorization while person-knowledge can promote individuation and enhance person memory. Although there is extensive literature on the cross-race recognition deficit, observed when racial ingroup faces are recognized more than outgroup faces, it is unclear whether a similar deficit exists when recalling individuating information about outgroup members. To better understand how perceived race can bias person memory, the present study examined how self-identified White perceivers’ interracial contact impacts learning of perceptual cues and person-knowledge about perceived Black and White others over five sessions of training. While person-knowledge facilitated face recognition accuracy for low-contact perceivers, face recognition accuracy did not differ for high-contact perceivers based on person-knowledge availability. The results indicate a bias towards better recall of ingroup person knowledge, which decreased for high-contact perceivers across the five-day training but simultaneously increased for low-contact perceivers. Overall, the elimination of racial bias in recall of person-knowledge among high-contact perceivers amid a persistent cross-race deficit in face recognition suggests that contact may have a greater impact on the recall of person-knowledge than on face recognition.

EGEFACE: A new face memory test with static and dynamic images

Face memory is a crucial cognitive ability necessary for maintaining a healthy social life. Recent studies reveal large individual differences in face recognition ability. Face memory tests are used to evaluate this ability. The main purpose of this study was to develop a new face memory test (EGEFACE) addressing the limitations of existing tests using both static and dynamic stimuli to increase ecological validity; employing face recognition algorithms to adjust test difficulty; measuring face memory accuracy independently of response bias by including both target-absent and target-present trials and using ROC analysis; and developing a test to measure both ends of the face recognition ability spectrum. After building a new database of static and dynamic faces, we created three difficulty levels using a face recognition algorithm. We collected data from 703 participants in two steps and examined the internal consistency, split-half reliability, and item–total score correlations. The reliability analysis confirmed that both target-absent and target-present trials of EGEFACE were reliable. High EGEFACE performers scored near super recognizer levels on CFMT+, while low performers showed limited overlap with prosopagnosic-level performance on CFMT+, suggesting EGEFACE’s sensitivity across different levels of face recognition ability. Overall, results indicated a moderate positive correlation between EGEFACE and CFMT+, showing that both tests assess similar cognitive skills, while a low to moderate correlation with KFMT suggests that EGEFACE measures cognitive ability that is related to yet distinct from face perception. The results suggest that EGEFACE shows promise as an ecologically valid and effective alternative tool for assessing individual differences in face memory.

Exploring predementia: Understanding the characteristics of subjective cognitive decline plus from India.

Subjective cognitive decline (SCD) is the early predementia syndrome. that occurs even before the development of objective cognitive decline. SCD plus refers to an additional set of criteria that increases the likelihood of developing mild cognitive impairment and further progressing to Alzheimer's disease (AD). Studying the progression of SCD-plus participants will help in understanding the importance of diagnosing this condition at an early stage and delaying its onset. The present tries to examine neurocognitive changes in individuals who met the criteria of SCD-plus patients. The study also investigated the imaging correlates of these individuals in both cohorts. This study included 94 participants from Srinivaspura Aging, Neuro Senescence, and COGnition (SANSCOG) and Tata Longitudinal Study of Aging (TLSA) cohorts who satisfy the criteria of SCD plus. Mann-Whitney U test was used to compare the SCD plus participants and healthy controls. Regression analysis was performed to find the association between SCD plus and cognition. The SCD-plus group performed poorer than the healthy group in episodic memory delayed recall (p = 0.049), name face recognition (p = 0.023), and letter fluency (p = 0.004) tasks. The generalized linear model revealed that the SCD-plus group had lower left cerebellar cortex (p = 0.010) and right inferior occipital cortex (p = 0.016) volumes than the healthy control group. The participants in the SCD-plus group performed poorly on memory and language-related tasks, and the volumes of the associated brain regions decreased. This study suggested that the SCD-plus group had characteristics similar to AD group and can help in identifying AD at the earliest.

Assessing bias and computational efficiency in vision transformers using early exits

Face recognition with deep learning is generally approached as a problem of capacity. The field has seen progressively deeper, more complex models or larger, more highly variant data sets. The data sets can be problematic, as they are often scraped indiscriminately from the internet. This results in an uncertain, and often heavily unbalanced distribution of race, gender, age and other aspects of the subjects, which is then manifested in the decisions of the models trained on them. The carbon footprint of machine learning is a concern. A real push is developing to reduce the energy consumption of machine learning as we strive for a more eco-friendly society. In addition, due to many instances of misuse by law enforcement and other agencies, unbiased models for face recognition are now fundamental to the practical application of the field. We present an approach using the state of the art Vision Transformer and Early Exits for reducing compute budget without significantly affecting performance. We develop a system for face recognition and identification with a closed-set gallery and show that with a small reduction in performance, a reasonable reduction in compute cost can be obtained using our method. Second, we investigate how these early Exits interact with the bias model through a robust evaluation of matching scores on a racially balanced data set. We show that matching scores vary heavily between cohorts, and these variations are magnified at the earlier exits.

Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50

In today's rapidly evolving world, where technology is progressing swiftly, there is an increasing demand for facial recognition systems. Technologies are similar to digital forensics in that they can recognize people by scanning faces. However, one key problem they confront is dealing with covered or occluded faces, which might restrict recognition of faces in real-world situations. To overcome this issue, we created a system that is capable of identifying individuals even when their faces are veiled. We used the face detector algorithm called Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection with 99.8% accuracy. Further we have conducted feature extraction and pre-processing on our self-created dataset. Our project utilizes the power of deep learning model: Residual Network (ResNet50), the form of deep neural network architectures well-suited for the job of features extraction. These features are matched by using Cosine similarity with accuracy of 92%. By leveraging the capabilities in the deep learning algorithms, this project provides a robust solution for automating the recognition of partially occluded faces.

Fraud Detection in Banking using Key Agreement and Face Authentication

Fraud detection in banking is a critical domain for financial security, driven by the rising frequency and sophistication of cyber-attacks. This paper introduces a dual-layered fraud detection framework combining key agreement protocols and face authentication. The system ensures secure communication through cryptographic techniques, such as Diffie-Hellman and Elliptic Curve Cryptography, while leveraging real-time facial recognition powered by TensorFlow and OpenCV for biometric authentication. These technologies collectively address identity theft, unauthorized access, and data breaches in banking operations. Implementation results demonstrate a high accuracy rate in user verification, efficient transaction processing, and robust protection against cyber threats. This paper outlines the system's architecture, implementation, and performance while addressing associated challenges such as privacy concerns, algorithmic biases, and scalability. The findings highlight the transformative potential of integrating cryptography and biometric authentication for modern banking security.

Contribution of neuroimaging to the understanding of social cognition in epilepsy.

This narrative review aims to identify and summarize existing research to better understand the pathophysiological and neuroanatomical bases of social cognition deficits in people with epilepsy. The neuroanatomical basis of social cognition was primarily examined in healthy subjects. In healthy individuals, social cognition is supported by a complex network of interconnected brain regions. Facial emotion recognition relies on a distributed set of structures, including the occipitotemporal neocortex, the temporoparietal and prefrontal areas, and the putamen with a pivotal role of the amygdala. Theory of mind primarily involves the dorsal medial prefrontal cortex and temporoparietal junction, while empathy engages the anterior insular and cingulate cortices. In people with epilepsy, most functional neuroimaging studies have focused on facial emotion recognition, primarily in patients with temporal lobe epilepsy. Nevertheless, across various domains of social cognition, abnormal activations and disrupted connectivity within social cognition networks are consistently observed, regardless of the focus location. Aberrant connectivity has also been noted in the few studies involving patients with generalized epilepsy. In focal epilepsy, the amygdala remains a central region for facial emotion recognition, irrespective of whether the epilepsy is localized to the temporal or frontal lobes. For theory of mind studies, regions typically identified in healthy individuals, such as the medial prefrontal cortex, exhibit either hyperactivation or reduced activation in people with focal epilepsy, complicating interpretation. In the domain of empathy, a study involving patients with idiopathic generalized epilepsy reported decreased activation in core regions commonly identified in healthy individuals, particularly the anterior cingulate cortex and anterior insula. The limited data available in the literature suggest that key regions shared between social cognition and epilepsy networks consistently contribute to these disruptions and may serve as potential targets for future neuromodulation interventions.

The Alexithymia Hypothesis of Autism Revisited: Alexithymia Modulates Social Brain Activity During Facial Affect Recognition in Autistic Adults.

Autism Spectrum Disorder (ASD) and alexithymia are both linked to difficulties in facial affect recognition (FAR) alongside differences in social brain activity. According to the Alexithymia Hypothesis, difficulties in emotion processing in ASD can be attributed to increased levels of co-occurring alexithymia. Despite substantial evidence supporting the hypothesis at the behavioral level, the effects of co-occurring alexithymia on brain function during FAR remain unexplored. Data from 120 participants (60 ASD, 60 controls) who completed an FAR task were analyzed using functional magnetic resonance imaging and behavioral measures. The task included both explicit and implicit measures of FAR. Autistic participants were further categorized based on their alexithymia status. Group differences in FAR performance and associated brain activation were investigated. Autistic participants showed lower FAR performance compared to controls, regardless of alexithymia status. Imaging revealed three cortical clusters with reduced activation in alexithymic compared to non-alexithymic ASD participants during explicit FAR, including the left inferior parietal gyrus, cuneus, and middle temporal gyrus. During implicit FAR, alexithymic ASD participants showed three cortical clusters of increased activation, including the left precentral gyrus, right precuneus, and temporoparietal junction. Our study shows an unexpected dissociation between behavior and brain response: While ASD affects FAR performance, only co-occurring alexithymia modulates corresponding social brain activations. Though not supporting the Alexithymia Hypothesis on the behavioral level, the study highlights the complex relationship between ASD and co-occurring alexithymia, emphasizing the significance of co-occurring conditions in understanding emotion processing in ASD.

Enhancing highway safety with LSTM-based facial emotion recognition

Enhancing highway safety with LSTM-based facial emotion recognition

Study on the Impact of LDA Preprocessing on Pig Face Identification with SVM.

In this study, the implementation of traditional machine learning models in the intelligent management of swine is explored, focusing on the impact of LDA preprocessing on pig facial recognition using an SVM. Through experimental analysis, the kernel functions for two testing protocols, one utilizing an SVM exclusively and the other employing a combination of LDA and an SVM, were identified as polynomial and RBF, both with coefficients of 0.03. Individual identification tests conducted on 10 pigs demonstrated that the enhanced protocol improved identification accuracy from 83.66% to 86.30%. Additionally, the training and testing durations were reduced to 0.7% and 0.3% of the original times, respectively. These findings suggest that LDA preprocessing significantly enhances the efficiency of individual pig identification using an SVM, providing empirical evidence for the deployment of SVM classifiers in mobile and embedded systems.

The use of facial recognition for targeting under international law

Abstract In the quest for “identity dominance” over the enemy, armed forces are increasingly leveraging biometrics for a variety of purposes. This paper focuses on the combat employment of one of them – facial recognition, which, unlike other biometrics, does not appear to have been widely utilized for targeting purposes yet. With the purchasing patterns of advanced militaries suggesting that such a development is around the corner, this paper assesses the compliance with international humanitarian law (IHL) of the use of facial recognition technologies for targeting purposes. It peruses the applicable legal framework to demonstrate that IHL is neutral towards the use of new technologies and that the right to privacy under international human rights law does not preclude the use of biometrics in hostilities. The analysis zooms in on two specific use cases in which facial recognition is likely to be employed on the battlefield, namely (1) targeted killings against combatants and (2) targeted killings against civilians directly participating in hostilities. The paper closes with an acknowledgment that while facial recognition does have obvious operational benefits, it also has the potential to exacerbate targeting practices that stretch the limits of IHL.

LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace.

For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface's weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications.

Integration of Yolov5 for Enhanced Driver Safety through Facial Recognition and Drowsiness Monitoring

Integration of Yolov5 for Enhanced Driver Safety through Facial Recognition and Drowsiness Monitoring

Enhancing Facial Expression Recognition with Robust CNN Architectures and Adaptive Preprocessing Techniques

Facial expression recognition (FER) is an essential technology at the intersection of artificial intelligence (AI), computer vision, and psychology. This study proposes a novel framework for FER, aiming to improve system robustness and generalization, especially under variable real-world conditions. Using the FER2013 dataset, this research combines an adaptive preprocessing pipeline with a custom Convolutional Neural Network (CNN) architecture. Key preprocessing steps include normalization, rotation, and flipping to improve data quality and diversity. The CNN architecture combines regularization methods, including dropout and L2 regularization. Dynamic hyperparameter tuning and early stopping optimize performance and prevent overfitting. Normalized confusion matrix indicating strong recognition for well-represented emotions, such as happiness with 86% accuracy, and challenges with underrepresented categories like disgust. This research aims to contribute to the ongoing development of facial expression recognition systems by enhancing their robustness and generalization. While further refinement is needed, this work provides a step toward more accurate and adaptable FER models, with the potential to support advancements in human-computer interaction and various real-world applications.

THE ROLE OF AI IN ENHANCING IDENTITY AND ACCESS MANAGEMENT SYSTEMS

In the modern digital world, safeguarding sensitive data and managing access to critical systems are essential for organizations. Identity and Access Management (IAM) is a key framework for controlling access to systems and data, ensuring only authorized users can gain access. Traditionally relying on static methods like passwords, IAM systems are now facing challenges due to the complexity of cyber threats and the increasing number of users and devices. To address these issues, AI is transforming IAM by improving user authentication, detecting security anomalies, and refining permission management. AI contributes to user authentication through biometric technologies like facial recognition, fingerprint scanning, and voice recognition, reducing vulnerabilities from traditional methods. Machine learning also enhances authentication by continuously analyzing user behavior, adapting systems to recognize legitimate users more accurately. Additionally, AI plays a vital role in anomaly detection by analyzing user activity data across various platforms and identifying unusual patterns that indicate potential threats. AI's impact extends to dynamic and context-aware permission management, offering real-time adjustments based on factors such as user role and location. Furthermore, AI supports continuous risk assessment and regulatory compliance by monitoring user activities and ensuring proper access controls. The integration of AI in IAM also strengthens cloud security, as seen in the research of Muppa (2022), Mandru (2022), Oduri (2019), Mohammed (2021), Ramakrishnan (2021), and Subburaman (2022), who explore how AI helps mitigate emerging threats, optimize authentication, and improve access control in cloud environments. Ultimately, AI in IAM offers a more adaptive, resilient, and precise solution to evolving security challenges. Organizations adopting AI-powered IAM systems will be better equipped to face future cyber threats, ensuring both data protection and operational continuity.

Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays

Convolutional neural networks (CNNs) are increasingly recognized as an important and potent artificial intelligence approach, widely employed in many computer vision applications, such as facial recognition. Their importance resides in their capacity to acquire hierarchical features, which is essential for recognizing complex patterns. Nevertheless, the intricate architectural design of CNNs leads to significant computing requirements. To tackle these issues, it is essential to construct a system based on field-programmable gate arrays (FPGAs) to speed up CNNs. FPGAs provide fast development capabilities, energy efficiency, decreased latency, and advanced reconfigurability. A facial recognition solution by leveraging deep learning and subsequently deploying it on an FPGA platform is suggested. The system detects whether a person has the necessary authorization to enter/access a place. The FPGA is responsible for processing this system with utmost security and without any internet connectivity. Various facial recognition networks are accomplished, including AlexNet, ResNet, and VGG-16 networks. The findings of the proposed method prove that the GoogLeNet network is the best fit due to its lower computational resource requirements, speed, and accuracy. The system was deployed on three hardware kits to appraise the performance of different programming approaches in terms of accuracy, latency, cost, and power consumption. The software programming on the Raspberry Pi-3B kit had a recognition accuracy of around 70–75% and relied on a stable internet connection for processing. This dependency on internet connectivity increases bandwidth consumption and fails to meet the required security criteria, contrary to ZYBO-Z7 board hardware programming. Nevertheless, the hardware/software co-design on the PYNQ-Z2 board achieved an accuracy rate of 85% to 87%. It operates independently of an internet connection, making it a standalone system and saving costs.