Facial Information Research Articles

Early Impairment of Face Perception in Post-Stroke Depression: An ERP Study.

Objective: Face recognition is an important cognitive function of the human brain. Post stroke depression (PSD) is a common mental complication after stroke, which has a serious impact on individual physical function recovery and quality of life. This study aims to explore the face perception characteristics of PSD through electrophysiological indicators N170 and VPP, and provide an objective basis for the early evaluation of facial cognitive dysfunction in PSD. Methods: 58 patients in the cerebral small vessel disease (CSVD) with depressive symptoms (PSD) and 188 patients in the pure CSVD (NPSD). At the same time, 30 healthy subjects were selected as the healthy controls (HC). The differences of N170 and VPP components between the three groups were compared under the stimulation of inverted faces and upright faces. Results: PSD patients exhibited significantly longer peak latency and lower amplitude of N170 and VPP under both inverted and upright face stimulation compared to HC and NPSD. These results suggest that PSD patients have defects in early face recognition, there are abnormalities in the early perception and structural encoding of face information, and both the "overall mechanism" and "feature mechanism" of face recognition are damaged. Conclusions: These findings provide neuroelectrophysiological evidence for impaired emotionless face recognition in PSD patients.

DISP-08. GBM PATIENTS FACE TTF ACCESS AND INFORMATION INEQUITY THAT MAY IMPACT PATIENT OUTCOMES AND QUALITY-OF-LIFE

Abstract We evaluated the quality and accessibility of healthcare for Glioblastoma (GBM) patients across the United States through an online survey distributed to the OurBrainBank community and partners and promoted on social media. A total of 297 patients or caregivers completed the survey (85% caregivers; 15% GBM patients). Median age at diagnosis was 57, lower than the national average. 78% described having some college education, and 69% reported commercial insurance coverage. Our preliminary findings highlight that 82% of respondents reported that their treating doctor recommended the standard-of-care for GBM consisting of tumor resection, 3-6 weeks of chemo/radiation, and 6 months of chemotherapy and sometimes Tumor-Treating-Fields (TTF), per the Comprehensive- Cancer-Network (2021) guidelines. 40% reported no discussion of TTF as an option. Of those offered TTF, nearly half (46%) received it as part of their initial treatment combined with standard chemotherapy and 36% received it as a subsequent option. For those attempting to access TTF, 29% encountered difficulties, often due to financial constraints linked to insurance denials. These findings illuminate significant concerns regarding dissemination of information on treatment options, as well as access to treatments. Access to these treatments can have a significant influence on survival and quality of life of GBM patients. The substantial number of patients not offered TTF, combined with challenges to accessing treatments due to insurance issues, highlight critical obstacles to engaging in informed and shared decision-making. Compared to the general GBM-affected population, our survey respondents tend to be younger and from urban areas, indicating a possible selection bias in survey participation. This skew could lead to an underrepresentation of the challenges encountered by patients facing additional barriers. We are adopting measures to engage a more diverse participant group, especially from rural communities Ongoing targeted recruitment will further elucidate barriers to patient-centered care in GBM, with the potential for significant improvements in survival and QOL.

Learning face super-resolution through identity features and distilling facial prior knowledge

Deep learning techniques in electronic surveillance have shown impressive performance for super-resolution (SR) of captured low-quality face images. Most of these techniques adopt facial priors to improve the feature details in the resultant super-resolved images. However, the estimation of facial priors from the captured low-quality images is often inaccurate in real-life situations because of their tiny, noisy, and blurry nature. Thus, the fusion of such priors badly affects the performance of these models. Therefore, this work presents a teacher–student-based face SR framework that efficiently preserves the personal facial structure information in the super-resolved faces. In the proposed framework, the teacher network exploits the facial heatmap-based ground-truth-prior to learn the facial structure that is utilized by the student network. The student network is trained with the identity feature loss for maintaining the identity and facial structure information in reconstructed high-resolution (HR) face images. The performance of the proposed framework is evaluated by conducting the experimental study on standard datasets namely CelebA-HQ and LFW face. The experimental results reveal that the proposed technique conquers the existing methods for the face SR task.

Analysis of emotions of online car-hailing drivers under different driving conditions and scenarios

Emotion is an important factor that affects driving behavior, and thus, drivers’ emotions are closely related to overall traffic safety. We investigated the emotional expressions of online car-hailing drivers under two driving conditions: with passenger(s) and without passenger(s). We recruited 16 male car-hailing drivers and collected a total of 91.5 h of data using non-contact equipment. We employed FaceReader 8.0 software to analyze the collected video data and extract the drivers’ facial expression information, thereby identifying six emotions expressed by the drivers. We then compared the frequency of the occurrence of the six emotions between the two conditions. The frequency rates indicate that the drivers exhibited more emotions when no passengers were in the vehicle. The chi-square test results indicate significant differences in the drivers’ emotions under the two conditions. For example, happiness is related to chatting with passengers. Also, the drivers exhibited more aggressive driving behavior during trips without passengers, and such behavior often was accompanied by negative emotions, such as anger. We also investigated drivers’ emotions under three scenarios that often occur while online car-hailing drivers are working: driver distractions, passenger interaction, and the traffic environment. The understanding of drivers’ emotions and the relationships between those emotions and scenarios that take place under different driving conditions can facilitate the identification of drivers’ intentions and provide guidance for the development of safe driving assistance warning systems. Dangerous driving behavior can be reduced through intervention and the monitoring of drivers’ emotions for the enhanced overall safety of roadway travel.

댓글분석을 활용한 사이코패스 인식: 웹크롤링과 LDA를 중심으로

This study explored the perceptions of psychopath among the general public in South Korea by collecting comments through web crawling. To uncover the hidden structures and automatically extract topics, we utilized Latent Dirichlet Allocation (LDA) for topic modeling. A total of 29,126 comments were collected for the final analysis, and through the preprocessing of unstructured natural language data, 7,519 unique words were identified. The results revealed four distinct topics. Topic 1 was named 'Negative perception and demand for capital punishment' as it primarily contained negative words about psychopaths and calls for the death penalty. Topic 2, which was dominated by words related to judges' rulings, was named 'Expectations and concerns about judicial decisions.' Topic 3 was named 'Demands for personal information disclosure,' consisting mainly of words related to the disclosure of personal and facial information. Topic 4 was named 'Words associated with psychopath,' as it included words frequently mentioned in conjunction with psychopath. This research identified several misconceptions about psychopath. It was observed that the general public tends to view psychopath as a mental illness that could justify a reduced sentence, and they perceive psychopath as irredeemable. Thus, this study contributes to enhancing the understanding of psychopath by capturing the social perception and diversity related to it.

ADYOLOv5-Face: An Enhanced YOLO-Based Face Detector for Small Target Faces

Benefiting from advancements in generic object detectors, significant progress has been achieved in the field of face detection. Among these algorithms, the You Only Look Once (YOLO) series plays an important role due to its low training computation cost. However, we have observed that face detectors based on lightweight YOLO models struggle with accurately detecting small faces. This is because they preserve more semantic information for large faces while compromising the detailed information for small faces. To address this issue, this study makes two contributions to enhance detection performance, particularly for small faces: (1) modifying the neck part of the architecture by integrating a Gather-and-Distribute mechanism instead of the traditional Feature Pyramid Network to tackle the information fusion challenges inherent in YOLO-based models; and (2) incorporating an additional detection head specifically designed for detecting small faces. To evaluate the performance of the proposed face detector, we introduce a new dataset named XD-Face for the face detection task. In the experimental section, the proposed model is trained using the Wider Face dataset and evaluated on both Wider Face and XD-face datasets. Experimental results demonstrate that the proposed face detector outperforms other excellent face detectors across all datasets involving small faces and achieved improvements of 1.1%, 1.09%, and 1.35% in the AP50 metric on the WiderFace validation dataset compared to the baseline YOLOv5s-based face detector.

A Student Facial Expression Recognition Model Based on Multi-Scale and Deep Fine-Grained Feature Attention Enhancement.

In smart classroom environments, accurately recognizing students' facial expressions is crucial for teachers to efficiently assess students' learning states, timely adjust teaching strategies, and enhance teaching quality and effectiveness. In this paper, we propose a student facial expression recognition model based on multi-scale and deep fine-grained feature attention enhancement (SFER-MDFAE) to address the issues of inaccurate facial feature extraction and poor robustness of facial expression recognition in smart classroom scenarios. Firstly, we construct a novel multi-scale dual-pooling feature aggregation module to capture and fuse facial information at different scales, thereby obtaining a comprehensive representation of key facial features; secondly, we design a key region-oriented attention mechanism to focus more on the nuances of facial expressions, further enhancing the representation of multi-scale deep fine-grained feature; finally, the fusion of multi-scale and deep fine-grained attention-enhanced features is used to obtain richer and more accurate facial key information and realize accurate facial expression recognition. The experimental results demonstrate that the proposed SFER-MDFAE outperforms the existing state-of-the-art methods, achieving an accuracy of 76.18% on FER2013, 92.75% on FERPlus, 92.93% on RAF-DB, 67.86% on AffectNet, and 93.74% on the real smart classroom facial expression dataset (SCFED). These results validate the effectiveness of the proposed method.

The 2024 Richardson Lecture: Prosopagnosia - A Classic Neurologic Deficit Meets the Modern Era.

Acquired prosopagnosia is a rare disorder, but it serves as a model for impairments in expert-level visual processing. This review discusses five key observations made over the past 30 years. First, there are variants, an apperceptive type linked to damage to the inferior occipitotemporal cortex and an amnestic type associated with anterior temporal lesions, both either right or bilateral. Second, these variants are clustered in syndromes with other perceptual deficits, the apperceptive type with field defects, dyschromatopsia and topographagnosia, and the amnestic type with topographagnosia and the auditory disorders of phonagnosia and acquired amusia. Third, extensive testing often shows additional problems with recognizing exemplars of other objects, especially when degrees of expertise are taken into account. Fourth, the prosopagnosic impairment does not affect all facial information. For example, the perception of expression and lip-reading likely depends on other neural substrates than those for processing facial identity. Last, face perception in prosopagnosia is not immutable but can improve with extensive training, though as yet this does not represent a cure for the condition. Continuing work with neural networks and animal models will enhance our understanding of this intriguing condition and what it tells us about how our brains process vision.

Face and face pareidolia in patients with temporal lobe epilepsy indicates different neural processing: an event-related potential study

BackgroundVisual perception of face images or face pareidolia can be evaluated with event-related potentials (ERP) for healthy subjects and patients with neurological conditions. In this study, we aimed to analyse event-related potential components such as P100, N100, N170, and vertex-positive potential (VPP) in response to face pareidolia perception in temporal lobe epilepsy (TLE) patients.MethodsERPs were recorded during the pareidolia test. Waveforms were analzyed and current source density (CSD) maps were generated.ResultsCSD profiles were shown to be interpretable when face and face pareidolia conditions. N100, P100, and N170 components showed larger amplitudes and longer latency in epilepsy patients in response to face pareidolia stimuli compared to real face images. However, the N170 component latency did not differ significantly between epilepsy patients and healthy participants, while the larger amplitude and longer latency of N100 and P100 responses were evoked in healthy patients.ConclusionsOur results indicate a difference in the neural mechanisms of processing real face information and pareidolia face-like information in TLE patients.

Horizontal face information is the main gateway to the shape and surface cues to familiar face identity.

Humans preferentially rely on horizontal cues when recognizing face identity. The reasons for this preference are largely elusive. Past research has proposed the existence of two main sources of face identity information: shape and surface reflectance. The access to surface and shape is disrupted by picture-plane inversion while contrast negation selectively impedes access to surface cues. Our objective was to characterize the shape versus surface nature of the face information conveyed by the horizontal range. To do this, we tracked the effects of inversion and negation in the orientation domain. Participants performed an identity recognition task using orientation-filtered (0° to 150°, 30° steps) pictures of familiar male actors presented either in a natural upright position and contrast polarity, inverted, or negated. We modelled the inversion and negation effects across orientations with a Gaussian function using a Bayesian nonlinear mixed-effects modelling approach. The effects of inversion and negation showed strikingly similar orientation tuning profiles, both peaking in the horizontal range, with a comparable tuning strength. These results suggest that the horizontal preference of human face recognition is due to this range yielding a privileged access to shape and surface cues, i.e. the two main sources of face identity information.

Neuroimaging studies of resting-state functional magnetic resonance imaging in eating disorders

Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), and pica, are psychobehavioral conditions characterized by abnormal eating behaviors and an excessive preoccupation with weight and body shape. This review examines changes in brain regions and functional connectivity in ED patients over the past decade (2013–2023) using resting-state functional magnetic resonance imaging (rs-fMRI). Key findings highlight alterations in brain networks such as the default mode network (DMN), central executive network (CEN), and emotion regulation network (ERN). In individuals with AN, there is reduced functional connectivity in areas associated with facial information processing and social cognition, alongside increased connectivity in regions linked to sensory stimulation, aesthetic judgment, and social anxiety. Conversely, BED patients show diminished connectivity in the dorsal anterior cingulate cortex within the salience network and increased connectivity in the posterior cingulate cortex and medial prefrontal cortex within the DMN. These findings suggest that rs-fMRI could serve as a valuable biomarker for assessing brain function and predicting treatment outcomes in EDs, paving the way for personalized therapeutic strategies.

Image sensor fusion for multimodal biometric recognition in mobile devices

AimMultimodal biometric recognition authenticates or identifies someone using various biometric features or modalities. Multimodal biometric systems use different biometric qualities to improve the recognition process's accuracy, security, and reliability rather than depending solely on one biometric property, such as fingerprint or facial recognition. Sensor fusion techniques can merge data from several biometric sensors, including fingerprint scanners and facial recognition cameras, to boost the accuracy and dependability of biometric authentication in the context of multimodal biometric recognition in mobile devices. The process of combining data from numerous image sensors to enhance a system's overall performance is known as image sensor fusion. ChallengesChallenges includeintegration difficulties in achieving accurate and secure multimodal biometric recognition on mobile devices. The study goal was Image sensor fusion for multimodal biometric recognition in mobile devices. MethodologyThe study uses a specially made database of 450 face photos roughly 450 fingerprints.Weiner filter (WF) is used for preparing data. Battle Royale optimized with deep convolutional neural network (BRO-DCNN) is proposed to improve the overall performance of fingerprint and facial information. FindingsAccuracy, sensitivity, specificity, and precision are used to measure the effectiveness of the proposed BRO-DCNN system. Study results show that BRO-MLANN solves the many characteristics of smart cities and is an efficient approach compared to other current methods in recognition rate, 98 % accuracy, 99 % precision, 96 % specificity, and 94 % sensitivity.

“Our Privacy Needs to be Protected Equally as Everybody's”—A Preliminary Study of Crowdsourced Delivery Riders' Privacy Concerns in China

ABSTRACTDelivery riders belong to crowd workers in the gig economy, representing a vulnerable group. Their privacy concerns and protection have not been duly investigated. To address this research problem, we surveyed to examine the issue of crowdsourced delivery riders' privacy concerns in China. It was found that these riders had often experienced privacy breaches and expressed major privacy concerns, such as the leakage of facial recognition information and contact details. Moreover, they lacked sufficient awareness of the Crowdwork platform's surveillance and knowledge of the Personal Information Protection Law (PIPL) in China. It is suggested that PIPL target vulnerable groups in addition to the general public, and privacy research in Human‐Computer Interaction (HCI) in China should focus on vulnerable populations like crowdsourced delivery riders.

Feasibility of using 3D facial information to predict phase angle for complications in hospitalized patients

Feasibility of using 3D facial information to predict phase angle for complications in hospitalized patients

A Fast Face Changing Method Adaptable to Various Postures

Image face-changing technology is influenced by skin color and posture. After changing faces, the contour information or skin color information of the user's face will be lost, resulting in low similarity between the changed image and the user's face in the source image. This paper introduces a rapid face-changing method that can adapt to various poses. Regardless of whether the facial poses in the source image and template image differ significantly, this method can retain the facial contours and skin tone in the user's source image without deformation, resulting in a high similarity between the transformed image and the face in the source image. Additionally, it offers low computational complexity, quick processing speed, and a small amount of computational effort.

Using 3D facial information to predict malnutrition and consequent complications.

Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL). From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital. Subjective global assessment was used as the gold standard of malnutrition diagnosis. The highest Youden index value was selected as the PhA cutoff point. We developed a multimodal DL framework to automatically analyze the three-dimensional (3D) facial data and accurately determine patients' PhA categories. The framework was trained and validated using a cross-validation approach and tested on an independent dataset. Four hundred eighty-two patients were included in the final dataset, including 176 with malnourishment. In male patients, the PhA value with the highest Youden index was 5.55°, and the area under the receiver operating characteristic curve (AUC) = 0.68; in female patients, the PhA value with the highest Youden index was 4.88°, and AUC = 0.69. Inpatients with low PhA had higher incidence of infectious complications during the hospital stay (P = 0.003). The DL model trained with 4096 points extracted from 3D facial data had the best performance. The algorithm showed fair performance in predicting PhA, with an AUC of 0.77 and an accuracy of 0.74. Predicting the PhA of inpatients from facial images is feasible and can be used for malnutrition assessment and prognostic prediction.

Information sampling differences supporting superior face identity processing ability.

Face recognition in humans is often cited as a model example of perceptual expertise that is characterized by an increased tendency to process faces as holistic percepts. However emerging evidence across different domains of expertise points to a critical role of feature-based processing strategies during the initial encoding of information. Here, we examined the eye-movement patterns of super-recognisers-individuals with extremely high faceidentification ability compared with the average person-using gaze-contingent "spotlight" apertures that restrict visual face information in real time around their point of fixation. As an additional contrast, we also compared their performance with that of facial examiners-highly trained individuals whose superiority has been shown to rely heavily on featural processing. Super-recognisers and facial examiners showed equivalent facematching accuracy in both spotlight aperture and natural viewing conditions, suggesting that they were equally adept at using featural information for faceidentity processing. Further, both groups sampled more information across the face than controls. Together, these results show that the active exploration of facial features is an important determinant of facerecognition ability that generalizes across different types of experts.

Influence of information anxiety on core competency of registered nurses: mediating effect of digital health literacy

BackgroundIn the information age of health care, nurses often face information overload, leading to negative emotions, e.g., anxiety that may impede the adoption of evidence-based practice and clinical decision-making process. Nurses with higher digital health literacy can effectively process and manage information. Despite this, no research has explored the relationship between information anxiety, digital health literacy, and core competency among nurses. Therefore, this study aims to investigate the mediating effects of digital health literacy on information anxiety and core competency among nurses.MethodsFrom July to October 2023, the data for this cross-sectional study were collected. The study surveyed a total of 608 nurses from three tertiary hospitals in Fujian Province, and the survey instruments included a sociodemographic information questionnaire, Chinese revision version of the Digital Health Literacy Instrument (CR-DHLI), Information Anxiety Scale (IAS), and Competency Inventory for Registered Nurses (CIRN). Descriptive statistics and Pearson correlation analysis were conducted using SPSS 29.0, and the mediating effect of digital health literacy was examined using Mplus.ResultsThe mean score of nurses’ information anxiety, digital health literacy, and core competency was 3.03 ± 0.91, 2.46 ± 0.56, 2.72 ± 0.88, respectively. And the mediation model of information anxiety on core competency for nurses showed a good model fit index (χ²/df = 2.207, CFI = 0.985, TLI = 0.982, RMSEA = 0.045, SRMR = 0.035). Digital health literacy was positively correlated with nurses’ core competency but negatively correlated with information anxiety. The results of path analysis revealed that information anxiety had negative and significant direct effects on NCC (β = -0.119, P = 0.004) and DHL (β = -0.297, P < 0.001). DHL had a positive effect on NCC (β = 0.306, P < 0.001). Digital health literacy played a partial mediating role, accounting for 43.54% of the relationship between information anxiety and nurses’ core competency.ConclusionsInformation anxiety among nurses was at relatively high levels, which had a negative impact on the core competency of nurses. This issue requires attention from nursing managers. The mediating role of digital health literacy in the relationship between information anxiety and core competency among nurses has been established. Nursing managers should strengthen the evaluation of nurses’ DHL and devise effective support strategies to enhance DHL, thus improving the core competence of nurses in information age.

Self-supervised reconstruction of re-renderable facial textures from single image

Reconstructing high-fidelity 3D facial texture from a single image is a quite challenging task due to the lack of complete face information and the domain gap between the 3D face and 2D image. Further, obtaining re-renderable 3D faces has become a strongly desired property in many applications, where the term ’re-renderable’ demands the facial texture to be spatially complete and disentangled with environmental illumination. In this paper, we propose a new self-supervised deep learning framework for reconstructing high-quality and re-renderable facial albedos from single-view images in the wild. Our main idea is to first utilize a prior generation module based on the 3DMM proxy model to produce an unwrapped texture and a globally parameterized prior albedo. Then we apply a detail refinement module to synthesize the final texture with both high-frequency details and completeness. To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation that is reconstructed with the detailed albedo together. We also design several novel regularization losses on both the albedo and illumination maps to facilitate the disentanglement of these two factors. Finally, by leveraging a differentiable renderer, each face attribute can be jointly trained in a self-supervised manner without requiring ground-truth facial reflectance. Extensive comparisons and ablation studies on challenging datasets demonstrate that our framework outperforms state-of-the-art approaches.

An Analysis of Farmer’s Perception Towards WhatsApp as a Farm Technology Dissemination Tools

Quick dissemination of technological information from agricultural research systems to the farmers and reporting of farmers’ feedback to the research system has always been one of the critical inputs in transfer of agricultural technologies leading to increased production. At present the ratio of farmers to extension workers is very less. The cost factor in face to face information dissemination at the right time and the difficulties in reaching the target audience have also created the urgency to introduce Information and Communication Technology (ICT). Agricultural news and communication be spread using social media platforms popular in the farming communities [1]. Social media is essential for closing the communication gap and instantly disseminating information to anyone anywhere in the world. Social media networks are used to share and discuss user-generated content such as opinions, videos, audio and multimedia. These networks are widely accessible, even in remote areas of the country. Among the many social media platforms, WhatsApp is especially useful for forming groups of people with similar interests, enabling the sharing of information. For farmers, WhatsApp has not only become a tool for information dissemination but also created a kind of support system. Hence, the present study was taken to assess the farmer’s perception towards WhatsApp as a farm technology dissemination tools. In this study was conducted in Cuddalore district of Tamil Nadu with the sample size of 100 respondents. The results indicated that more than two-third (68.00 %) of the respondents had high level of perception towards WhatsApp.