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When looking at faces, humans invariably move their eyes to a consistent preferred first fixation location on the face. While most people have the preferred fixation location just below the eyes, a minority have it between the nose-tip and mouth. Not much is known about whether these long-term differences in the preferred fixation location are associated with distinct neural representations of faces. To study this, we used a gaze-contingent face adaptation aftereffect paradigm to test in two groups of observers, one with their mean preferred fixation location closer to the eyes (upper lookers) and the other closer to the mouth (lower lookers). In this task, participants were required to maintain their gaze at either their own groups mean preferred fixation location or that of the other group during adaptation and testing. The two possible fixation locations were 3.6° apart on the face. We measured the face adaptation aftereffects when the adaptation and testing happened while participants maintained fixation at either the same or different locations on the face. Both groups showed equally strong adaptation effects when the adaptation and testing happened at the same fixation location. Crucially, only the upper lookers showed a partial transfer of the FAE across the two fixation locations, when adaptation occurred at the eyes. Lower lookers showed no spatial transfer of the FAE irrespective of the adaptation position. Given the classic finding that neural tuning is increasingly position invariant as one moves higher in the visual hierarchy, this result suggests that differences in the preferred fixation location are associated with distinct neural representations of faces.

A central assumption of neuroscience is that long-term memories are represented by the same brain areas that encode sensory stimuli1. Neurons in inferotemporal (IT) cortex represent the sensory percept of visual objects using a distributed axis code2-4. Whether and how the same IT neural population represents the long-term memory of visual objects remains unclear. Here we examined how familiar faces are encoded in the IT anterior medial face patch (AM), perirhinal face patch (PR) and temporal pole face patch (TP). In AM and PR we observed that the encoding axis for familiar faces is rotated relative to that for unfamiliar faces at long latency; in TP this memory-related rotation was much weaker. Contrary to previous claims, the relative response magnitude to familiar versus unfamiliar faces was not a stable indicator of familiarity in any patch5-11. The mechanism underlying the memory-related axis change is likely intrinsic to IT cortex, because inactivation of PR did not affect axis change dynamics in AM. Overall, our results suggest that memories of familiar faces are represented in AM and perirhinal cortex by a distinct long-latency code, explaining how the same cell population can encode both the percept and memory of faces.

We used a reverse-correlation image-classification paradigm to visualize facial representations of immigrants and citizens in the United States. Visualizations of immigrants' faces were judged by independent raters as less trustworthy and less competent and were more likely to be categorized as a non-White race/ethnicity than were visualizations of citizens' faces. Additionally, image generators' personal characteristics (e.g., implicit and explicit evaluations of immigrants, nativity status) did not reliably track with independent judges' ratings of image generators' representations of immigrants. These findings suggest that anti-immigrant sentiment and racial/ethnic assumptions characterize facial representations of immigrants in the United States, even among people who harbor positivity toward immigrants.

Faces are among the most important visual stimuli that humans perceive in everyday life. While extensive literature has examined emotional processing and social evaluations of faces, most studies have examined either topic using unimodal approaches. In this review, we promote the use of multimodal cognitive neuroscience approaches to study these processes, using two lines of research as examples: ambiguity in facial expressions of emotion and social trait judgment of faces. In the first set of studies, we identified an event-related potential that signals emotion ambiguity using electroencephalography and we found convergent neural responses to emotion ambiguity using functional neuroimaging and single-neuron recordings. In the second set of studies, we discuss how different neuroimaging and personality-dimensional approaches together provide new insights into social trait judgments of faces. In both sets of studies, we provide an in-depth comparison between neurotypicals and people with autism spectrum disorder. We offer a computational account for the behavioral and neural markers of the different facial processing between the two groups. Finally, we suggest new practices for studying the emotional processing and social evaluations of faces. All data discussed in the case studies of this review are publicly available.

This article studies customers entering automated self-service hotels in China and using a facial recognition kiosk for registration. Based on video recordings of 674 cases of customers checking in, we show that, as is common in self-service, customers need to do work that was previously done by hotel staff: They are working customers. We then argue that, when interacting with the facial recognition kiosk, customers are also doing something more: First, they present themselves to the machine by, for example adjusting their standing position or appearance; second, they perform for the machine by following its instructions, closing their eyes or opening their mouths; finally, they express various emotions towards the machine, such as anger or embarrassment. In sum, we show that customers are working not just with their bodies but also on their bodies, which are being disciplined by the machine. They become 'disciplined customers'. [ABSTRACT FROM AUTHOR]

In response to the security management issues of student apartments, a study is conducted on a student apartment access control system based on multitasking cascaded convolutional networks and FaceNet. Firstly, a face detection model is built based on an improved multi-task cascaded convolutional network, and then a face recognition model is built using FaceNet. The results showed that the detection accuracy of the multi-task cascaded convolutional network using the improved non-maximum suppression algorithm was 98.7%, which was higher than the traditional multi-task cascaded convolutional network and effectively improved the detection performance of the multi-task cascaded convolutional network. The face detection model based on the improved multi-task cascaded convolutional network had the shortest average detection time of 361s, the highest average detection accuracy of 90.3%, an accuracy of 99%, a recall rate of 98.5%, and an F1 value of 99%. While maintaining high detection efficiency, it also ensured the accuracy of detection. The average accuracy of the mask detection method based on the MobileNet V2 network was relatively high, at 98.96%. The facial recognition model based on FaceNet achieved a recognition accuracy of 99.15% for faces without masks and 92.04% for faces with masks, with the highest accuracy and recall rates of 99.3% and 99.6%. The model constructed in the study has good application effects in face detection, which helps to improve the security of the student apartment access control system. [ABSTRACT FROM AUTHOR]

Myasthenia gravis (MG) is a neuromuscular disease of autoimmune etiology and chronic evolution. In addition to the muscle weakness and fatigue that characterize MG, in some studies patients show an inferior performance in cognitive tasks and difficulties in recognizing basic emotions from facial expressions. However, it remains unclear if these difficulties are due to anxious–depressive symptoms that these patients present or related to cognitive abilities, such as facial recognition. This study had a descriptive cross-sectional design with a sample of 92 participants, 52 patients with MG and 40 healthy controls. The data collection protocol included measures to assess recognition of facial expressions (BRFT), facial emotional expression (FEEL), and levels of anxiety and depression (HADS). The MG group had worse performance than the control group in recognizing "fear" (p = 0.001; r = 0.344), "happiness" (p = 0.000; r = 0.580), "disgust" (p = 0.000; r = 0.399), "surprise" (p = 0.000; r = 0.602), and "anger" (p = 0.007; r = 0.284). Likewise, the MG group also underperformed in facial recognition (p = 0.001; r = 0.338). These difficulties were not related to their levels of anxiety and depression. Alterations were observed both in the recognition of facial emotions and in facial recognition, without being mediated by emotional variables. These difficulties can influence the interpersonal interaction of patients with MG. [ABSTRACT FROM AUTHOR]

Facial keypoint detection technology faces significant challenges under conditions such as occlusion, extreme angles, and other demanding environments. Previous research has largely relied on deep learning regression methods using the face's overall global template. However, these methods lack robustness in difficult conditions, leading to instability in detecting facial keypoints. To address this challenge, we propose a joint optimization approach that combines regression with heatmaps, emphasizing the importance of local apparent features. Furthermore, to mitigate the reduced learning capacity resulting from model pruning, we integrate external supervision signals through knowledge distillation into our method. This strategy fosters the development of efficient, effective, and lightweight facial keypoint detection technology. Experimental results on the CelebA, 300W, and AFLW datasets demonstrate that our proposed method significantly improves the robustness of facial keypoint detection. [ABSTRACT FROM AUTHOR]

Genetic disorders affect over 6% of the global population and pose substantial obstacles to healthcare systems. Early identification of these rare facial genetic disorders is essential for managing related medical complexities and health issues. Many people consider the existing screening techniques inadequate, often leading to a diagnosis several years after birth. This study evaluated the efficacy of deep learning-based classifier models for accurately recognizing dysmorphic characteristics using facial photos. This study proposes a multi-class facial syndrome classification framework that encompasses a unique combination of diseases not previously examined together. The study focused on distinguishing between individuals with four specific genetic disorders (Down syndrome, Noonan syndrome, Turner syndrome, and Williams syndrome) and healthy controls. We investigated how well fine-tuning a few well-known convolutional neural network (CNN)-based pre-trained models—including VGG16, ResNet-50, ResNet152, and VGG-Face—worked for the multi-class facial syndrome classification task. We obtained the most encouraging results by adjusting the VGG-Face model. The proposed fine-tuned VGG-Face model not only demonstrated the best performance in this study, but it also performed better than other state-of-the-art pre-trained CNN models for the multi-class facial syndrome classification task. The fine-tuned model achieved both accuracy and an F1-Score of 90%, indicating significant progress in accurately detecting the specified genetic disorders. [ABSTRACT FROM AUTHOR]

Bias in facial recognition systems often results in unequal performance across demographic groups. This study addresses this by investigating how dataset composition affects the performance and bias of age estimation models across ethnicities. We fine-tuned pre-trained Convolutional Neural Networks (CNNs) like VGG19 on the diverse UTKFace dataset (23,705 samples: 10,078 White, 4526 Black, 3434 Asian) and APPA-REAL (7691 samples: 6686 White, 231 Black, 674 Asian). Our approach involved adjusting dataset compositions by oversampling minority groups or reducing samples from overrepresented groups to mitigate bias. We conducted experiments to identify the optimal dataset composition that minimizes performance disparities among ethnic groups. The primary performance metric was Mean Absolute Error (MAE), measuring the average magnitude of prediction errors. We also analyzed the standard deviation of MAE across ethnic groups to assess performance consistency and equity. Our findings reveal that simple oversampling of minority groups does not ensure equitable performance. Instead, systematic adjustments, including reducing samples from overrepresented groups, led to more balanced performance and lower MAE standard deviations across ethnicities. These insights highlight the importance of tailored dataset adjustments and suggest exploring advanced data processing methods and algorithmic tweaks to enhance fairness and accuracy in facial recognition technologies. [ABSTRACT FROM AUTHOR]

As more and more companies adopt artificial intelligence to increase the efficiency and effectiveness of their products and services, they expose themselves to ethical crises and potentially damaging public controversy associated with its use. Despite the prevalence of AI ethical problems, most companies are strategically unprepared to respond effectively to the public. This paper aims to advance our empirical understanding of company responses to AI ethical crises by focusing on the rise and fall of facial recognition technology. Specifically, through a comparative case study of how four big technology companies responded to public outcry over their facial recognition programs, we not only demonstrated the unfolding and consequences of public controversies over this new technology, but also identified and described four major types of company responses—Deflection, Improvement, Validation, and Pre-emption. These findings pave the way for future research on the management of controversial technology and the ethics of AI. [ABSTRACT FROM AUTHOR]

Facial recognition is an artificial intelligence-based technology that, like many other forms of artificial intelligence, suffers from an accuracy deficit. This paper focuses on one particular use of facial recognition, namely identification, both as authentication and as recognition. Despite technological advances, facial recognition technology can still produce erroneous identifications. This paper addresses algorithmic identification failures from an upstream perspective by identifying the main causes of misidentifications (in particular, the probabilistic character of this technology, its 'black box' nature and its algorithmic bias) and from a downstream perspective, highlighting the possible legal consequences of such failures in various scenarios (namely liability lawsuits). In addition to presenting the causes and effects of such errors, the paper also presents measures that can be deployed to reduce errors and avoid liabilities. [ABSTRACT FROM AUTHOR]

Effective anonymous facial registration techniques are critical to address privacy concerns arising from facial recognition technology. This study presents an intelligent anonymity platform that incorporates blockchain with advanced privacy and uses a CIAGAN-powered approach. This solution addresses the immediate need for privacy in facial recognition technology. The proposed system uses advanced techniques to anonymously generate highly realistic and effective facial images. The widespread use of facial recognition systems places greater emphasis on privacy concerns, emphasizing the need for strong enrollment mechanisms. The proposed system uses CIAGAN to address this challenge and generate facial images while preserving important attributes. Blockchain storage ensures that data integrity and security are maintained. The process begins with detailed image preprocessing steps to improve data quality and eliminate unwanted noise. CIAGAN can generate anonymous face images with important facial attributes to complicate the recognition of specific objects. A dataset of 202,599 facial images was used. Performance metrics such as PSNR and SSIM indicate image quality and uniformity. The PSNR obtained was 35.0516, indicating a unique image anonymization process. [ABSTRACT FROM AUTHOR]

Face recognition technology is gaining popularity for security, access management, and user identification. A novel facial recognition method employing cutting-edge deep learning algorithms and attention processes reduces false positives in this study. This technique was designed to approach facial recognition differently. We demonstrate statistically substantial recognition gains over current approaches through extensive research and experimentation. The recommended solution uses an attention device and a complex feature extraction module. The pieces work together to highlight distinctive characteristics and facial identifiers. To optimize performance and generalization across datasets, data addition and hyper parameter adjustment are used to finetune the model. We do this for maximum benefit. Studies on the issue may help us understand the multiple reasons that make ablation so successful. We also discuss facial recognition technology's moral difficulties, including fairness and user privacy. We also emphasize cautious distribution. Our findings expand facial recognition technology knowledge and pave the way for future studies. This study demonstrates that better Mobile Net models and Internet of Things technologies increase the accuracy of mobile facial recognition. The project overcomes the challenge of providing powerful AI tools in resource-constrained situations by utilizing IoT infrastructures and effective, lightweight Mobile Net architectures. Extensive testing demonstrates that the technique increases identification rates and outperforms existing models, showing its suitability for real-time operations. The Internet of Things enables data mobility and cross-device model usage. This illustrates that the IoT ecosystem can enable effective and scalable security solutions. [ABSTRACT FROM AUTHOR]

Aviation information systems are a key component in ensuring efficient and smooth air transport operations. In this regard, the transfer of passenger information between parties is of paramount importance. With the continuous improvement of biometrics technology, this kind of individual identification that can provide accurate and unforgeable identification is widely used in various fields. This research presents the significance and effective application scenarios of facial recognition in biometrics in air transport operations. Due to the characteristics of aviation information systems, Distributed Ledger Technology (DLT) is used in this study for secure and private transmission of facial recognition information. Distributed systems can give a transparent and secure platform to multiple parties to access sensitive passenger data. This study uses the Corda framework as the DLT that supports CorDapp development. Based on the above techniques, this study proposes two feasible application scenarios. One is a baggage match detection system to prevent misplaced baggage, and the other is an iAPIS system that transmits passenger information in real-time communication between airlines and border control agencies. This article details how to apply the research in these two scenarios, as well as the benefits and implications of the applications. Finally, this article presents an outlook for future development and feasible directions for improvement. [ABSTRACT FROM AUTHOR]

Harbor seals, Phoca vitulina, play a critical role in regulating the biodiversity of coastal ecosystems in the North Pacific and Atlantic Oceans. We conducted a preliminary ecological study of harbor seals in Casco Bay, Maine using SealNet, a newly developed facial recognition software. We captured images of seals on nine haul-out sites to create a database of 768 seals in Middle Bay. We used photo ID techniques with facial recognition technology to record the location of individuals at each haul-out site. We calculated a range of 9% site fidelity to the Middle Bay inlet across years and 25% and 36% seasonal site fidelity to haul-out sites within 2020 and 2021, respectively. Preliminary estimates of the local seal abundance within Middle Bay ranged from 1562 (single haul-out site) to 2533 seals (across sites and years). In addition, our results suggest that the number of seals at haul-out sites is greatest from two hours before low tide to two hours after low tide and during high cloud cover conditions. We found no significant impacts of water or air temperature, level of boat traffic, or wind speed on haul-out site abundance. Our study supports the use of facial recognition technology as an effective method to monitor dynamic coastal species. The aim of future research will focus on a more systematic, longitudinal study design to monitor specific haul-out sites with the aim of providing more extensive connectivity data between sites and more refined estimates of site fidelity, turnover, and population size. [ABSTRACT FROM AUTHOR]

Automated Teller Machines (ATMs) are widely used for cash withdrawals and other banking transactions. However, their touchscreens pose a significant health risk due to potential contamination with various pathogens. Studies have shown that ATMs can harbor bacteria and viruses linked to diseases like influenza (25%), E. coli (18%), and Staphylococcus aureus (14%). This research proposes Gesture Vault, a touchless ATM system that leverages facial and hand gesture recognition technologies. Gesture Vault addresses hygiene concerns by eliminating the need for physical contact with the ATM screen. Facial recognition ensures secure user identification, while hand gestures provide an intuitive interface for account selection and transaction initiation. This type of authentication also strengthens security compared to traditional PIN-based systems. Gesture Vault promotes a hygienic and secure banking experience, potentially reducing the spread of infectious diseases and improving user confidence in using ATMs. [ABSTRACT FROM AUTHOR]

All Educational Institutions prioritize student attendance and Baze University is certainly one to uphold the standard. All students are required to maintain a minimum attendance rate of at least 75% in each class or lecture to be eligible to take the module's examination. With the advancement of technology, biometrics concepts have proven to be more efficient in monitoring attendance. We present a facial recognition-based prototype webapp for Baze university. This webapp aims to eradicate the cumbersome method of marking attendance that lecturers of Baze University have subscribed to. The main focus is the use of facial recognition techniques using the Dlib library of OpenCV. Images are read and stored in a database, compared with images captured from a webcam to analyze and record the best matches using nearest neighbor algorithm. [ABSTRACT FROM AUTHOR]

The blockchain has continued to showcase myriads of promises as a breakthrough tech and highly adapted in manifold sectors. Its adoption is accompanied by a range of issues that make implementation complicated. To aid successful implementation, a variety of frameworks have been developed. But, selecting the appropriate framework based on the conformity of its features with the financial sector is a challenge for decision-makers. This study seeks to provide solution to the challenge of synchronizing entire blockchain in a minimum possible time, and create a Blockchain Technology Framework for pricing and Evaluating Financial Markets and also simulate the cryptocurrency market data using a neural network based application to generate the optimal prediction and predicting the future of investment by fusing the facial recognition system with the framework. [ABSTRACT FROM AUTHOR]

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Today, more than ever before, technological progress is evolving rapidly, and in the absence of adequate regulatory frameworks, the big players in the digital market (the so-called Big Techs) are exploiting personal data (name, address, telephone numbers) and private data (political opinions, religious beliefs, financial information, or health status) in an uncontrolled manner. A crucial role in this scenario is played by the weakness of international regulatory frameworks due to the slow response time of legislators who are incapable, from a regulatory point of view, of keeping pace with technological evolution and responding to the new requirements coming from the social context, which is increasingly characterized by the pervasive presence of new technologies, such as smartphones and wearable devices. At the European level, the General Data Protection Regulation (GDPR) and the Regulation on Electronic Identification, Authentication and Trust Services (eIDAS) have marked a significant turning point in the regulatory landscape. However, the mechanisms proposed present clear security issues, particularly in light of emerging concepts such as digital identity. Moreover, despite the centrality of biometric issues within the European regulatory framework and the practical introduction of biometric data within electronic national identity (eID) cards, there are still no efforts to use biometric features for the identification and authentication of a person in a digital context. This paper clarifies and precisely defines the potential impact of biometric-based digital identity and hypothesizes its practical use for accessing network-based services and applications commonly used in daily life. Using the Italian eID card as a model, an authentication scheme leveraging biometric data is proposed, ensuring full compliance with GDPR and eIDAS regulations. The findings suggest that such a scheme can significantly improve the security and reliability of electronic identification systems, promoting broader adoption of eIDAS solutions. [ABSTRACT FROM AUTHOR]

Total-body PET/CT images can be rendered to produce images of a subject's face and body. In response to privacy and identifiability concerns when sharing data, we have developed and validated a workflow that obscures (defaces) a subject's face in 3-dimensional volumetric data. Methods: To validate our method, we measured facial identifiability before and after defacing images from 30 healthy subjects who were imaged with both [18F]FDG PET and CT at either 3 or 6 time points. Briefly, facial embeddings were calculated using Google's FaceNet, and an analysis of clustering was used to estimate identifiability. Results: Faces rendered from CT images were correctly matched to CT scans at other time points at a rate of 93%, which decreased to 6% after defacing. Faces rendered from PET images were correctly matched to PET images at other time points at a maximum rate of 64% and to CT images at a maximum rate of 50%, both of which decreased to 7% after defacing. We further demonstrated that defaced CT images can be used for attenuation correction during PET reconstruction, introducing a maximum bias of -3.3% in regions of the cerebral cortex nearest the face. Conclusion: We believe that the proposed method provides a baseline of anonymity and discretion when sharing image data online or between institutions and will help to facilitate collaboration and future regulatory compliance.