Human Face Research Articles

A human digital twin approach for fatigue-aware task planning in human-robot collaborative assembly

Human-robot collaboration (HRC) has emerged as a pivotal paradigm in manufacturing, integrating the strengths of both human and robot capabilities. Neglecting human physical fatigue may adversely affect worker health and, in extreme cases, may lead to musculoskeletal disorders. However, human fatigue has rarely been considered for decision-making in HRC manufacturing systems. Integrating adaptive decision-making to optimise human fatigue in HRC manufacturing systems is crucial. Nonetheless, real-time perception and estimation of human fatigue and decision-making informed by human fatigue face considerable challenges. To address these challenges, this paper introduces a human digital twin method, a bidirectional communication system for physical fatigue assessment and reduction in human-robot collaborative assembly tasks. The methodology encompasses an IK-BiLSTM-AM-based surrogate model, which consists of inverse kinematics analysis (IK), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), for real-time muscle force estimation integrated with a muscle force-fatigue model for muscle fatigue assessment. An And-Or graph and optimisation model-based HRC task planner is also developed to alleviate physical fatigue via task allocation. The efficacy of this approach has been validated through proof-of-concept assembly experiments involving multiple subjects. The results show that the IK-BiLSTM-AM model achieves a minimum of 8% greater accuracy in muscle force estimation than the baseline methods. The 12-subject assessment results indicate that the task planner effectively reduces the physical fatigue of workers while performing collaborative assembly tasks.

Human Face Recognition Based Employee Attendance System

Human Face Recognition Based Employee Attendance System

The Significance of Artificial Intelligence in Learning and Education Management in the Light of Philosophy of Education: A Critical Appraisal

The integration of Artificial Intelligence (AI) into the framework of education has spelt out a remarkable landmark in the history of educational revolution. Both in teaching-learning methodologies and management practice, AI is rapidly becoming a pivotal indispensability. Employing qualitative methods of conceptual frame, content analysis and literature review, this paper critically evaluates the significance of AI in learning and education management in the light of philosophy of education. The highlights of the paper include the concepts of AI, learning, education management, and philosophy of education. Philosophical perspectives and ethical considerations in implementing AI technology are also discussed. The capacity of AI to inform personalized learning, enhance intelligent learning, and facilitate data-driven decision making is critically examined. The paper, going forward, explores the transformational role of teachers amid the reality of the AI environmental permeation and centrality, with a focus on the need for teachers to be adequately equipped with requisite skills that promote critical thinking, creativity and innovation. In the process of the critical appraisal, it is discovered that AI has great potentials to engender harmonized and customized learning experiences. It is also discovered that it equally brings, in its wake, hordes of challenges and concerns with regard to the possibility of stripping the human face of education in terms of depersonalization, ethical concerns, danger of data privacy and algorithmic bias. On the basis of the findings, the paper recommends, among others, that stakeholders in education should adopt a balanced approach to the issue of AI in learning and education management. In this model of approach, AI is to be given an ancillary position as a source of information and knowledge in the process of education. In this way, AI will play a complementary role to the time-tested traditional values through which education strives to offer and achieve a comprehensive development of the learner. This will go a long way in addressing the challenges associated with AI, thereby harnessing its transformative potentials in learning and education management.

Attendance System for College Hostels Using Facial Recognition

Face detection is a computer vision technology designed to identify and locate human faces in digital images. It is a specialized application of object detection, which involves identifying instances of specific semantic objects, such as humans, buildings, or vehicles, in images and videos. With advancements in technology, face detection has become increasingly significant in fields like photography, security, and marketing. This report presents an efficient approach to detecting and recognizing human faces using OpenCV and Python. It explores the pivotal role of machine learning in computer science and its application in facial detection through various OpenCV libraries. Furthermore, the report proposes a system for real-time human face detection, leveraging the integration of machine learning techniques with OpenCV and Python.

Critical Ischemia Following Hyaluronic Acid Filler Injection: A Case Report

Background: Vascular complications, following the application of non-permanent, hyaluronic acid-based tissue fillers are a rare but very serious and rapidly progressive disorder that can, in extreme cases, lead to skin necrosis, blindness, or a stroke. Interest in aesthetic procedures is constantly growing, so awareness and knowledge of the correct and comprehensive treatment of complications are important. The human face is an area characterised by complex innervation and vascularisation. There are high-risk areas in which the application of fillers should be carried out with particular care using appropriate techniques and instruments, as well as preparations with specific rheology. The aetiopathogenesis of vascular complications is complex—involving partial or complete vessel occlusion, the presence of local inflammation in the affected tissues, and potential arteriospasm of the vessels supplying the area, resulting in tissue dysperfusion and ischaemia. Methods: In this article, the case of a patient who developed signs of a vascular compromise on the forehead area after improperly administering filler in the glabellar and nasal areas is presented. Result: The diagnostic and therapeutic management applied, including above all high doses of hyaluronidase, Doppler ultrasound diagnostics, and general medications, as well as a complementary treatment of the distant effects of the complication, i.e., erythema and tissue loss in the forehead area, by autologous injection procedures and laser therapy, resulted in a full recovery and a very good aesthetic result. Conclusions: This case proves that complications after aesthetic medicine procedures, including vascular complications are possible. Education of patients and doctors, proper diagnosis, and initiation of appropriate treatment at an early stage of the problem can bring very good therapeutic results for the patient.

Quantification of Facial Asymmetry: A Comprehensive Review of Methods and Applications

Facial asymmetry, present in all human faces at varying degrees, plays a critical role in clinical fields such as orthodontics, orthognathic and plastic surgeries, and craniofacial reconstruction. Accurate quantification of facial asymmetry is essential for diagnosis, treatment planning, and post-surgical evaluation. This article examines contemporary methods for quantifying facial asymmetry, including two-dimensional (2D) and three-dimensional (3D) landmark-based approaches, surface curvature analysis, and advanced image-based techniques. Emphasis is placed on the advantages, limitations, and applications of each method, to meet clinical or research needs. By integrating technological advancements, future developments are expected to enhance precision and applicability in clinical practice.

On face value: a ghost driver field study investigating interactions between pedestrians and a driverless vehicle with anthropomorphic displays

In a novel, on-road study, using a ‘Ghost Driver’ to emulate an automated vehicle (AV), we captured over 10 hours of video (n = 520) and 64 survey responses documenting the behaviour and attitudes of pedestrians in response to the AV. Three prototype external human-machine interfaces (eHMIs) described the AV’s behaviour, awareness and intention using elements of anthropomorphism: High (human face), Low (car motif), (partial representation of human features that lacked precise visual reference); these were evaluated against a (no eHMI) baseline. Despite many pedestrians reporting that they still relied on vehicular cues to negotiate their crossing, there was a desire/expectation expressed for explicit communication with future AVs. High and Low anthropomorphism eHMIs received the most positive responses for clarity, confidence and trust, with High also attracting significantly more/longer glances and the highest preference rating. In contrast, was considered least clear and subsequently invited the lowest confidence and trust ratings.

Exploring the Challenges of Aadhaar based Face Recognition in Unrestricted Environments

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

Investigating the intrinsic top-down dynamics of deep generative models

Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity at rest is the manifestation of top-down dynamics of generative models detached from action-perception cycles. A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms. In this work, we study the generative dynamics of a recent extension of the DBN, the iterative DBN (iDBN), which more faithfully simulates neurocognitive development by jointly tuning the connection weights across all layers of the hierarchy. We characterize the number of states visited during top-down sampling and investigate whether the heterogeneity of visited attractors could be increased by initiating the generation process from biased hidden states. To this end, we train iDBN models on well-known datasets containing handwritten digits and pictures of human faces, and show that the ability to generate diverse data prototypes can be enhanced by initializing top-down sampling from “chimera states”, which represent high-level features combining multiple abstract representations of the sensory data. Although the models are not always able to transition between all potential target states within a single-generation trajectory, the iDBN shows richer top-down dynamics in comparison to a shallow generative model (a single-layer Restricted Bolzamann Machine). We further show that the generated samples can be used to support continual learning through generative replay mechanisms. Our findings suggest that the top-down dynamics of hierarchical generative models is significantly influenced by the shape of the energy function, which depends both on the depth of the processing architecture and on the statistical structure of the sensory data.

The Documents of the Shōsōin Treasury and the History of Their Study

One of the most important features of the documents from the Shōsōin treasury is the opportunity for researchers not only to study the events, institutions, and sources of the Nara era, but also to see the Japanese 8th century “with a human face.” Among the documents, there is a lot of material that talks about the everyday life and service of the sovereign’s subjects. This is of special interest for the author of the article, who aims to see a person who lived in ancient Japan. To create a basis for future research, the author conducts a source study of the Shōsōin documents as a preparatory stage for their study.The objectives of the article are to discuss where and why the Shōsōin documents were created; find out how they were placed into the treasury and how they were stored in it; give their classification, describe the types of documents; find out what happened to them after the conservation of the Shōsōin at the end of the 8th century, how the documents were rediscovered in the Edo period, how and why they were studied in the 19th and 20th centuries; talk about the scholars who were involved in their research, and about the modern study of the Shōsōin documents.

FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models

Text-to-image generative models, especially those based on latent diffusion models (LDMs), have demonstrated outstanding ability in generating high-quality and high-resolution images from textual prompts. With this advancement, various fine-tuning methods have been developed to personalize text-to-image models for specific applications such as artistic style adaptation and human face transfer. However, such advancements have raised copyright concerns, especially when the data are used for personalization without authorization. For example, a malicious user can employ fine-tuning techniques to replicate the style of an artist without consent. In light of this concern, we propose FT-Shield, a watermarking solution tailored for the fine-tuning of text-to-image diffusion models. FT-Shield addresses copyright protection challenges by designing new watermark generation and detection strategies. In particular, it introduces an innovative algorithm for watermark generation. It ensures the seamless transfer of watermarks from training images to generated outputs, facilitating the identification of copyrighted material use. To tackle the variability in fine-tuning methods and their impact on watermark detection, FT-Shield integrates a Mixture of Experts (MoE) approach for watermark detection. Comprehensive experiments validate the effectiveness of our proposed FT-Shield.

Neurociencia y educación: comprendiendo el origen del aprendizaje desde la plasticidad cerebral

Neuroscience, as a vast field of study, is responsible for offering revolutionary perspectives on how the human brain learns and processes information. So this article delves into the contribution of this discipline in order to understand learning, thus proposing an innovative education that is based on the research of Jean Piaget, Paul McLean, Francisco Mora, among other prominent figures with an exploratory and descriptive methodology. Examining brain plasticity, that amazing ability of the brain to adapt and reorganize in response to new experiences and challenges that humans face throughout life. Taking into account the neural connections, which are strengthened or weakened with each new experience and are also fundamental to learning and the acquisition of knowledge. So in the development of the same, the importance of understanding the cognitive processes and brain areas that work in synergy to generate the conscious experience, and contribute to the functioning of the brain, resulting in learning, is highlighted. Understanding these mechanisms can therefore not only transform the way we teach in classrooms, but can also influence self-taught learning methods and settings outside of traditional educational settings. Neuroscience, therefore, not only illuminates areas of knowledge, but also offers tools to improve educational practices, making learning a more personalized and effective experience.

Systematic Review and Meta‐Analyses Reveal no Gender Difference in Neonatal Social Perception

ABSTRACTWomen score higher than men on measures of social cognition such as empathy and reading non‐verbal cues. How early does this gender difference emerge? Systematic review and meta‐analyses were used to assess gender difference in social perception within 1 month of birth. A total of 31 studies (40 experiments) reported on gender effects in 1936 neonates (50% girls) between 1968 and 2021. No significant difference (Hedges’ g = 0.076, p = 0.321) was found across 20 experiments measuring visual fixation on human faces. Nine experiments on neonatal imitative crying also revealed no gender difference (g = 0.157, p = 0.118). Seven studies using the Neonatal Behavioral Assessment Scale found no gender difference in total orientation (g = 0.161, p = 0.154), but girls oriented more to both animate (g = 0.279, p = 0.011) and inanimate (g = 0.242, p = 0.003) stimuli in the studies that analyzed these separately. Existing evidence supports a possible maturational difference but not a specific social advantage for girls at birth. While more research and better reporting are needed, the present findings challenge the claim that girls are innately more socially perceptive than boys.

Exploring the Human Face of Engaged Environmental Strategies: The Role of Chief Executive Officer and Human Capital in Peruvian Firms

ABSTRACTIn emerging economies characterized by the absence of strong national regulatory institutional forces, the role of private initiative through companies and entrepreneurs is highly relevant and has a prominent role in addressing environmental problems. This work aims to improve the understanding of the role of two key human‐based organizational factors, such as CEO environmental commitment and green human capital (GHC), on the effective implementation of corporate environmental strategies seeking to integrate and engage market and non‐market stakeholders. Our empirical research was carried out in a sample of 86 Peruvian firms operating in different industries through in‐depth interviews and questionnaires. Our results show the importance of CEO environmental commitment on the implementation of engaged environmental strategies, as well as the mediating and essential role of GHC in this relationship. We contribute to the literature debate by providing theory and empirical insights on the drivers of corporate environmentalism in developing countries, highlighting the importance of the human‐based internal organizational factors as driving forces of corporate environmentalism.

Cross-Validating the Electrophysiological Markers of Early Face Categorization.

Human face categorization has been extensively studied using event-related potentials (ERPs), positing the N170 ERP component as a robust neural marker of face categorization. Recently, the fast periodic visual stimulation (FPVS) approach relying on steady-state visual evoked potentials (SSVEPs) has also been used to investigate face categorization. FPVS studies consistently report strong bilateral SSVEP face categorization responses over the occipitotemporal cortex, with a right hemispheric dominance, closely mirroring the N170 scalp topography. However, it remains unclear whether SSVEP responses can be considered a proxy for the N170 or are driven by different components. To address this question, we recorded electrophysiological signals from observers viewing face and object images during FPVS and ERP paradigms. We quantified the FPVS response in the frequency domain and extracted ERP components, including the P1, N170, and P2, from both the FPVS time domain and ERP paradigms. Our results revealed little relationship between any single ERP component and the FPVS frequency response. Only the peak-to-peak differences between N170 and P2 components consistently explained the FPVS frequency response. Our data show that the FPVS frequency response reflects a later complex neural integration rather than any isolated ERP component, such as the N170. These findings raise important methodological and theoretical considerations regarding the relationship between SSVEPs and transient ERPs. While both markers are indicative of human face categorization, they appear to capture different stages of this cognitive process.

Employing Some Statistical Models in Facial Recognition

Face recognition is a computer vision approach that analyzes facial features to identify or verify a person's identity. it may be required to combine different biometric identification methods to improve security and recognition accuracy. To identify an individual, facial recognition systems simulate human face understanding and recognition abilities management, there is difficulty in detecting data when using various patterns of facial image capture, especially when there is a large overlap between the patterns, it takes time to determine a person's membership, Many statistical methods have been proposed in recent years, and in this study, will be employing the Support Vector Machine (SVM), which is a statistical model for face recognition that has been used for classification and regression tasks, so this model will be compared with logistic regression, one of the significant statistical models used in prediction, classification, and facial recognition. Various methods will be evaluated with a group of datasets and varied samples (number of images); this group represented the real data, which consisted of 100 individuals. The results showed that SVM had the lowest mean square error (MSE) for the dataset, followed by LR, and all the methods reached the highest accuracy rate of 100% for facial image recognition on real data.

Human vs machine learning in face recognition: a case study from the travel industry

This research was conducted to help answer whether a machine learning simulation can replace the human ability to recognize human faces, especially under challenges under travel industry requirements. The human ability to recognize faces was evaluated using a series of questions in a survey. The questions challenged the human respondents to recognize faces under similar looks, with hair and makeup disguises, only part of the facial area, and under dark lighting conditions. At the same time, a histogram of oriented gradient (HoG) combined with a support vector machine (SVM) was built for machine learning simulations. The machine learning was evaluated using two datasets, i.e., the Extended Yale B (EYB) Face dataset for challenge under dark lighting conditions and The Extended Makeup Face Dataset (EMFD) for challenge using face with makeup disguise. The results showed that machine learning simulation of the face recognition system yielded accuracy as high as 95.4% under dark lighting conditions and 70.8% under facial makeup disguise. On the contrary, only 48% of respondents accurately recognized human faces in dark lighting. The number was increased to 94-96% when the face images were adjusted first with the contrast adjustment method. However, only 36-37% of respondents accurately recognized human faces under face makeup disguise.

Identifying Opportunity Targets in Gram-Negative Pathogens for Infectious Disease Mitigation.

Antimicrobial drug resistance (AMR) is a pressing global human health challenge. Humans face one of their grandest challenges as climate change expands the habitat of vectors that bear human pathogens, incidences of nosocomial infections rise, and new antibiotics discovery lags. AMR is a multifaceted problem that requires a multidisciplinary and an "all-hands-on-deck" approach. As chemical microbiologists, we are well positioned to understand the complexities of AMR while seeing opportunities for tackling the challenge. In this Outlook, we focus on vulnerabilities of human pathogens and posit that they represent "opportunity targets" for which few modulatory ligands exist. We center our attention on proteins in Gram-negative organisms, which are recalcitrant to many antibiotics because of their external membrane barrier. Our hope is to highlight such targets and explore their potential as "druggable" proteins for infectious disease mitigation. We posit that success in this endeavor will introduce new classes of antibiotics that might alleviate some of the current pressing AMR concerns.

How the brain differentiates human and monkey faces: Neuromagnetic evidence from spatiotemporal dynamics

Face recognition is an important aspect of human non-verbal communication. Event-related potentials or magnetic fields, such as the N170/M170 component, are considered essential neural markers of face processing. Compared to upright human faces, inverted human faces and upright but not inverted animal faces cause longer latencies and larger amplitudes of these components. However, the mechanisms underlying this factor remain unclear. To elucidate the spatiotemporal dynamics of the processing of inverted human and animal faces, we recorded face-selective responses (M170) to upright and inverted human and monkey faces using a 306-channel whole-head magnetoencephalography. Sensor analysis showed an increased M170 latency and amplitude for inverted human and upright animal faces. However, in the source analysis, the observed modulations of the estimated spatiotemporal dynamics were different from the sensor results: irrespective of species, upright faces activated wider areas in the ventral and dorsal visual regions compared with inverted faces. Additionally, face orientation differentially modulated the anterior region of the fusiform gyrus (FG) in both face categories. These results suggest that spatiotemporal dynamics differ in face orientation regardless of category and that the FG contributes little or nothing to the M170 modulation recorded in the scalp sensor. Furthermore, we demonstrated that inverted human and animal faces are processed via different mechanisms.

View-symmetric representations of faces in human and artificial neural networks.

View symmetry has been suggested to be an important intermediate representation between view-specific and view-invariant representations of faces in the human brain. Here, we compared view-symmetry in humans and a deep convolutional neural network (DCNN) trained to recognise faces. First, we compared the output of the DCNN to head rotations in yaw (left-right), pitch (up-down) and roll (in-plane rotation). For yaw, an initial view-specific representation was evident in the convolutional layers, but a view-symmetric representation emerged in the fully-connected layers. Consistent with a role in the recognition of faces, we found that view-symmetric responses to yaw were greater for same identity compared to different identity faces. In contrast, we did not find a similar transition from view-specific to view-symmetric representations in the DCNN for either pitch or roll. These findings suggest that view-symmetry emerges when opposite rotations of the head lead to mirror images. Next, we compared the view-symmetric patterns of response to yaw in the DCNN with corresponding behavioural and neural responses in humans. We found that responses in the fully-connected layers of the DCNN correlated with judgements of perceptual similarity and with the responses of higher visual regions. These findings suggest that view-symmetric representations may be computationally efficient way to represent faces in humans and artificial neural networks for the recognition of identity.