Pictures Of Faces Research Articles

Estereótipos raciais e atratividade física facial: explorando suas implicações nas avaliações morais em saúde

Abstract In this study of 333 participants, we investigated how racial stereotypes and facial attractiveness impact moral evaluations in healthcare. We used pictures of faces with different levels of attractiveness in moral dilemma scenarios, performing statistical analyses such as ANOVA and ANCOVA to examine these complex interactions. We found that physical attractiveness positively influences moral evaluations only in low moral conflict scenarios and does not apply to high conflict or impersonal situations. The relationship between self-declared skin color and participants’ gender was only confirmed in a specific scenario, highlighting the complexity of these influences. Hypotheses three and four, which suggested that participants’ ethnic and moral identity would suppress the effects of racial stereotypes and facial attractiveness, were not confirmed. However, we observed that the ethnic profile of the faces and the participants’ motivation to control prejudice positively influenced moral evaluations. These results are interpreted in light of theories on interpersonal attraction, moral judgment, and intergroup relationships, providing essential insights into the complex dynamics that shape moral evaluations in healthcare.

Grip strength, working memory, and emotion perception in middle-aged males.

This study examined the association between grip strength and emotional working memory in middle-aged adults. Seventy-six males aged 40-60years (mean=48.5years, SD=5.4) participated in this cross-sectional study. They completed a muscular fitness assessment using a maximum grip strength test and emotional n-back tasks under two emotion conditions (fearful and neutral facial pictures) and two working memory loads (1-back and 2-back tasks). Hierarchical regression analyses indicated that greater muscular fitness was associated with superior working memory performance in the fearful condition in both the 1-back and 2-back tasks, after controlling for confounders. Greater muscular fitness was also associated with superior working memory performance in the neutral condition when the working memory load was high (2-back task) but not low (1-back task). These findings suggest a positive association between muscular fitness and emotional working memory and highlight the importance of maintaining muscular fitness for physical and cognitive-emotional well-being in middle-aged adults.

Revolutionizing Sales with Phone Systems: Design and Implementation Best Practices

The technique of classifying a face as recognized or unknown in computer vision is known as face recognition. The procedure follows a pipeline that includes steps for data collection, detection, pre-processing, and recognition. This research focuses on with the assumption that photos have already been processed at the end of the pipeline pre-processed and gathered Face recognition software in the past has relied on the full face. Their algorithms take a face picture as input. We propose a new method in which the individual facial segments, such as the left, are sent into the recognition algorithm. The four components that make up the face are the left eye, the right eye, the nose, and the mouth. After that, the output of each algorithm is labelled, and it is categorized as belonging to right feature class. According to the findings of our research, the granularity of information gathered for each subdivision class has the potential to be utilized in order to create a higher accuracy rate than can be achieved by using the complete face technique.

Resistance of a short-term memory concealed information test with famous faces to countermeasures

The concealed information test (CIT) aims at identifying knowledge that a person wants to hide, by measuring physiological indices during the presentation of known versus unknown items. Recently, Lancry-Dayan et al. (Journal of Applied Research in Memory and Cognition, 7 (2), 291–302, 2018) proposed a new version of this test that included a short-term memory task to maximize differences between responses to items. Participants were asked to memorize four pictures of faces that included one face of an acquaintance. The authors observed that participants looked at the familiar face during the first second and then tended to avoid it. This specific orientation-avoidance pattern occurred even in participants instructed to conceal their familiarity with the known faces (in a spontaneous or a guided manner). In a first experiment, we reproduced Lancry-Dayan et al.’s (2018) study using photos of famous faces. The pattern found by Lancry-Dayan et al. was observed in participants asked to perform the memory task only, participants asked to conceal their familiarity with the famous faces, and participants of a countermeasure group. In a second experiment, we tested the robustness of Lancry-Dayan et al.'s countermeasure. We modified the instructions by emphasizing the oculomotor task or giving feedback. While between-group differences in gaze-pattern appeared after feedback was provided, classification analyses were still able to distinguish between familiar and unfamiliar faces accurately, which revealed the good resistance of this new CIT protocol to countermeasures.

Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics.

Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty ('feature-based aesthetics'), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver's brain ('processing-based aesthetics'), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty.

Impressions from Faces, Personality Judgment, and Relevant Factors

The phrase "facial impression" describes how someone's first impression of another person's personality, state of mind, attitude, and other traits are dependent on how others express themselves facially. One's facial impression is mostly determined by the visual cues of one's emotions, such as the eyes, grin, and other facial features. Personality judgment is the in-depth assessment of a person's personality using indications from their behavior and social interactions, as well as visual cues from their face and other relevant aspects. Personality evaluation requires more complex cognitive and information processing that carefully considers all other non-facial variables in addition to analyzing face impressions. The relationship between facial impressions and personality evaluations is reciprocal. There is evidence that a person's personality qualities may be inferred from their facial features, however the precision of this association varies depending on the context. Previous research also discovered that face characteristics have an impact on people's social cognition and conduct. Therefore, face characteristics are crucial in creating initial impressions and assessing personality. People routinely assess the emotions and personality traits of others by analyzing their facial expressions, which is an essential part of human social interaction. This review can provide some guidance to prevention and intervention programs that aim to reduce social bias and discrimination.

Application of the Face2Gene tool in an Italian dysmorphological pediatric clinic: Retrospective validation and future perspectives.

Neurodevelopmental disorders exhibit recurrent facial features that can suggest the genetic diagnosis at a glance, but recognizing subtle dysmorphisms is a specialized skill that requires very long training. Face2Gene (FDNA Inc) is an innovative computer-aided phenotyping tool that analyses patient's portraits and suggests 30 candidate syndromes with similar morphology in a prioritized list. We hypothesized that the software could support even expert physicians in the diagnostic workup of genetic conditions. In this study, we assessed the performance of Face2Gene in an Italian dysmorphological pediatrics clinic. We uploaded two-dimensional face pictures of 145 children affected by genetic conditions with typical phenotypic traits. All diagnoses were previously confirmed by cytogenetic or molecular tests. Overall, the software's differential included the correct syndrome in most cases (98%). We evaluated the efficiency of the algorithm even considering the rareness of the genetic conditions. All "common" diagnoses were correctly identified, most of them with high diagnostic accuracy (93% in top-3 matches). Finally, the performance for the most common pediatric syndromes was calculated. Face2Gene performed well even for ultra-rare genetic conditions (75% within top-3 matches and 83% within top-10 matches). Expert geneticists maybe do not need computer support to recognize common syndromes, but our results prove that the tool can be useful not only for general pediatricians but also in dysmorphological clinics for ultra-rare genetic conditions.

Comparison of Deep Convolutional GAN and Progressive GAN for facial image generation

Generative Adversarial Networks (GANs) have generated realistic and diverse facial images with promising results. This work demonstrates a technique for creating facial pictures using GANs and evaluates the effectiveness of several GAN designs and training approaches. The CelebA dataset is leveraged for training and evaluating the GAN model, and employ a variety of evaluation metrics, such as the Structural Similarity Index (SSIM) to assess the quality and diversity of the generated images. Progressive GAN outperforms Deep Convolutional GAN in terms of image quality and diversity, and conditional GAN training is more effective than standard GAN training for generating facial images with specific attributes. The combination of Progressive GAN and conditional GAN training produces facial images of the utmost quality and diversity. The findings contribute to a broader comprehension of the use of GANs for generating facial images and have ramifications for a variety of applications, from facial recognition to virtual reality.

Deep dynamic face capture and recognition based on internet of things

AbstractFace recognition is widely used as identity authentication method in the Internet of Things (IoTs). However, compared with static camera, the background of IoT devices may change. Additionally, the facial expression or posture may change as well. Although face recognition has been researched for several decades, it is still a challenging task to design a robust face recognition system in IoT environment. This paper designs a special face recognition system by introducing deep transfer learning to adapt difference scenes in IoT environment. The face recognition system consists of two parts: terminal smart devices and cloud center. The terminal smart device consists of a camera to capture face picture, a wireless network part to communicate with cloud center, and a screen to show recognition result. The cloud center receives the face picture from terminal smart device and returns the face recognition result by using an offline deep transfer learning model. Compared with previous models, deep transfer learning model can better adapt different scenes. The experiments shows that the proposed face recognition system achieves more promising results than two source‐only models and two shallow transfer learning models.

Intracerebral electrical stimulation of the face-selective right lateral fusiform gyrus transiently impairs face identity recognition

Neuroimaging and intracranial electrophysiological studies have consistently shown the largest and most consistent face-selective neural activity in the middle portion of the human right lateral fusiform gyrus (‘fusiform face area(s)’, FFA). Yet, direct evidence for the critical role of this region in face identity recognition (FIR) is still lacking. Here we report the first evidence of transient behavioral impairment of FIR during focal electrical stimulation of the right FFA. Upon stimulation of an electrode contact within this region, subject CJ, who shows typical FIR ability outside of stimulation, was transiently unable to point to pictures of famous faces among strangers and to match pictures of famous or unfamiliar faces presented simultaneously for their identity. Her performance at comparable tasks with other visual materials (written names, pictures of buildings) remained unaffected by stimulation at the same location. During right FFA stimulation, CJ consistently reported that simultaneously presented faces appeared as being the same identity, with little or no distortion of the spatial face configuration. Independent electrophysiological recordings showed the largest neural face-selective and face identity activity at the critical electrode contacts. Altogether, this extensive multimodal case report supports the causal role of the right FFA in FIR.

Incidental learning of faces during threat: No evidence for enhanced physiological responses to former threat identities

Remembering an unfamiliar person and the contextual conditions of that encounter is important for adaptive future behavior, especially in a potentially dangerous situation. Initiating defensive behavior in the presence of former dangerous circumstances can be crucial. Recent studies showed selective electrocortical processing of faces that were previously seen in a threat context compared to a safety context, however, this was not reflected in conscious recognition performance. Here, we investigated whether previously seen threat-faces, that could not be remembered, were capable to activate defensive psychophysiological response systems. During an encoding phase, 50 participants with low to moderate levels of anxiety viewed 40 face pictures with neutral expressions (6 s each), without an explicit learning instruction (incidental learning task). Each half of the faces were presented with contextual background colors that signaled either threat-of-shock or safety. In the recognition phase, all old and additional new faces (total of 60) were presented intermixed without context information. Participants had to decide whether a face was new or had been presented previously in a threatening or a safe context. Results show moderate face recognition independent of context conditions. Startle reflex and skin conductance responses (SCR) were more pronounced for threat compared to safety during encoding. For SCR, this differentiation was enhanced with higher levels of depression and anxiety. There were no differential startle reflex or SCR effects during recognition. From a clinical perspective, these findings do not support the notion that perceptual biases and physiological arousal directly relate to threat-associated identity recognition deficits in healthy and clinical participants with anxiety and trauma-related disorders.

A Real Time Employee Attendance Monitoring System using ANN

Face recognition refers to the technology that examines and contrasts a person's face characteristics to recognise or verify their identity. Recently, this technology has drawn a lot of attention due to the potential uses it may have in security, marketing, and law enforcement. Face recognition involves studying a picture or video of a person's face to identify features like the space between their eyes, the contour of their nose, and the curve of their mouth. The person's identity is then established or verified by comparing these characteristics to a database of previously saved pictures. A series of techniques called facial recognition algorithms are used to identify and authenticate persons based on the features of their faces. These algorithms compare a person's facial attributes to those in a database of recognised faces by looking at things like the shape of their face, the distance between their eyes, and other distinctive facial features. There are many different types of face recognition algorithms, including geometric-based algorithms, appearance-based algorithms, and hybrid algorithms that combine both approaches. Geometric-based algorithms employ the geometry of face traits to identify and validate people, while appearance-based algorithms use image processing techniques to compare the patterns and textures of facial features. Recent advances in deep learning have significantly improved the accuracy of facial recognition algorithms. Artificial Neural Network (ANN) has shown to be highly effective and have been used in a range of applications, including mobile devices, security, and surveillance. Face recognition algorithms provide advantages, but there are also moral dilemmas with regard to its application, such as potential biases and privacy difficulties. As technology advances, it is imperative to address these problems and ensure that face recognition algorithms are used ethically and responsibly.

Effects of masculinity vs. femininity on competence judgement of politician faces and election outcome prediction

First impressions of politician faces can be effective in predicting election outcomes, based on perceived competence from candidate photographs. However, it remains unclear whether such effects arose from facial features or other non-facial information present in the photographs (e.g. hairstyles, clothes, or poses). In four pre-registered studies, participants completed two tasks in a counter-balanced order: rating competence of individually presented faces and predicting election outcome of each pair of winner and runner-up faces. We examined competence judgment and election outcome prediction on faces from male politicians depicted on original portraits (Experiment 1), or on computer-generated faces with facial features extracted from the portraits (Experiment 2). The faces were then either masculinized or feminized (Experiments 3 and 4). We found that competence ratings were significantly higher for winners than runners-up and that winners were more likely predicted to win the elections than runners-up in all but Experiment 4, where faces of the winners were feminized and faces of the runners-up were masculinized. Regardless of facial feature changes, correlations were found between competence ratings and election outcome prediction. These findings suggest that facial features are critical for evaluating competence and predicting election outcome, and that masculine features may enhance stereotypical leadership impressions.

Heritability of amygdala reactivity to angry faces and its replicable association with the schizophrenia risk locus of miR-137.

Among healthy participants, the interindividual variability of brain response to facial emotions is associated with genetic variation, including common risk variants for schizophrenia, a heritable brain disorder characterized by anomalies in emotion processing. We aimed to identify genetic variants associated with heritable brain activity during processing of facial emotions among healthy participants and to explore the impact of these identified variants among patients with schizophrenia. We conducted a data-driven stepwise study including samples of healthy twins, unrelated healthy participants and patients with schizophrenia. Participants approached or avoided pictures of faces with negative emotional valence during functional magnetic resonance imaging (fMRI). We investigated 3 samples of healthy participants - including 28 healthy twin pairs, 289 unrelated healthy participants (genome-wide association study [GWAS] discovery sample) and 90 unrelated healthy participants (replication sample) - and 1 sample of 48 patients with schizophrenia. Among healthy twins, we identified the amygdala as the brain region with the highest heritability during processing of angry faces (heritability estimate 0.54, p < 0.001). Subsequent GWAS in both discovery and replication samples of healthy non-twins indicated that amygdala activity was associated with a polymorphism in the miR-137 locus (rs1198575), a micro-RNA strongly involved in risk for schizophrenia. A significant effect in the same direction was found among patients with schizophrenia (p = 0.03). The limited sample size available for GWAS analyses may require further replication of results. Our data-driven approach shows preliminary evidence that amygdala activity, as evaluated with our task, is heritable. Our genetic associations preliminarily suggest a role for miR-137 in brain activity during explicit processing of facial emotions among healthy participants and patients with schizophrenia, pointing to the amygdala as a brain region whose activity is related to miR-137.

Single neuron responses underlying face recognition in the human midfusiform face-selective cortex

Faces are critical for social interactions and their recognition constitutes one of the most important and challenging functions of the human brain. While neurons responding selectively to faces have been recorded for decades in the monkey brain, face-selective neural activations have been reported with neuroimaging primarily in the human midfusiform gyrus. Yet, the cellular mechanisms producing selective responses to faces in this hominoid neuroanatomical structure remain unknown. Here we report single neuron recordings performed in 5 human subjects (1 male, 4 females) implanted with intracerebral microelectrodes in the face-selective midfusiform gyrus, while they viewed pictures of familiar and unknown faces and places. We observed similar responses to faces and places at the single cell level, but a significantly higher number of neurons responding to faces, thus offering a mechanistic account for the face-selective activations observed in this region. Although individual neurons did not respond preferentially to familiar faces, a population level analysis could consistently determine whether or not the faces (but not the places) were familiar, only about 50 ms after the initial recognition of the stimuli as faces. These results provide insights into the neural mechanisms of face processing in the human brain.

Zebrafish (Danio rerio) distinguish between two human caretakers and their associated roles within a captive environment

Zebrafish have become a popular biomedical model in recent decades, and are now maintained in laboratories worldwide. However, living within a captive environment can introduce novel environmental stressors and experiencing stress chronically can negatively affect animal welfare and, if applicable, how study results are interpreted. One method to potentially alleviate stress within captive environments is to introduce signaled predictability, i.e., conditioning captive populations to anticipate a future event by associating a visual or audio cue with the impending stimulus. A low-cost and easily accessible cue could be the caretakers themselves. Previous research has shown that archerfish can associate and distinguish between pictures of human faces, not human beings themselves. Therefore, it remains to be seen if fish, such as zebrafish can distinguish between two human caretakers, associated with different husbandry tasks. For this study, zebrafish were exposed to a caretaker who fed the fish only and a caretaker who chased the fish with a net, i.e., associated with an acute stressor. Caretaker duties were counterbalanced across two isolated laboratory shelving racks. Before performing tasks, the caretaker presented their face to all 10 fish tanks and then recorded the number of fish in the feeding area immediately. It was found that presentation of the stressor-caretaker did not significantly affect the number of fish within the feeding area, however, presentation of the caretaker associated with feeding led to a significant increase in the number of fish in the feeding area. By the end of the experiment, fish were more likely to be found in this area following the feeding-associated caretaker presentation, compared to the stressor-associated caretaker. These results suggest that zebrafish are capable of distinguishing between two human caretakers by learning to associate specific humans with husbandry procedures, particularly feeding. However, it is unclear exactly what visual cues, e.g., caretaker mannerisms, were utilized as caretaker faces were not isolated for presentation. Further research is needed to assess the repeatability of these results, as this research is novel, especially with respect to captive fish welfare.

Design and Implementation of IoT Based Smart Lecture Attendance System at Mataram University of Technology

Student attendance is one of the reporting activities that exist in educational institutions. The problem that occurs in educational institutions is that when entering the lecture, many students are late and often absent, which can cause discipline where students often do absenteeism, so lecturers cannot know the number of students who attend accurately. From these problems, a solution is needed to help lecturers recapitulate attendance data. This system uses ESP32 as a data manager, RFID for data reading, and ESP32 to validate student attendance by taking pictures of faces. The data is stored on the web server using ESP32CAM to cover the shortcomings of RFID, which is still card-based, so that it can emphasize the flaws. To simplify the attendance in this study, utilizing the website as an interface to facilitate lecturers in knowing the number of students who are present, late, or absent more efficiently and accurately

Evaluation of Facial Vitiligo Severity with a Mixed Clinical and Artificial Intelligence Approach

Vitiligo is the most common depigmenting skin disorder. Given the ongoing development of new targeted therapies, it has become important to evaluate adequately the surface area involved. Assessment of vitiligo scores can be time consuming, with variations between investigators. Therefore, the aim of this study was to build an artificial intelligence system capable of assessing facial vitiligo severity. One hundred pictures of faces of patients with vitiligo were used to train and validate the artificial intelligence model. Sixty-nine additional pictures of facial vitiligo were then used as a final dataset. Three expert physicians scored the facial vitiligo on the same 69 pictures. Inter and intrarater performances were evaluated by comparing the scores between raters and artificial intelligence. Algorithm assessment achieved an accuracy of 93%. Overall, the scores reached a good agreement between vitiligo raters and the artificial intelligence model. Results demonstrate the potential of the model. It provides an objective evaluation of facial vitiligo and could become a complementary/alternative tool to human assessment in clinical practice and/or clinical research.

Cross-cultural applicability of eye-tracking in assessing attention to emotional faces in preschool-aged children.

Humans show an attention bias toward emotional versus neutral information, which is considered an adaptive pattern of information processing. Deviations from this pattern have been observed in children with socially withdrawn behaviors, with most research being conducted in controlled settings among children from urban areas. The goal of the current study was to examine the cross-cultural applicability of two eye-tracking-based measures in assessing attention biases and their relations to children's symptoms of socially withdrawn behaviors in two independent and diverse samples of preschool children. The cross-cultural comparison was conducted between the Navajo Birth Cohort study (NBCS), an indigenous cohort with relatively low socioeconomic status (SES), and the Illinois Kids Development study (IKIDS), a primarily Non-Hispanic White and high SES cohort. Children in both cohorts completed eye-tracking tasks with pictures of emotional faces, and mothers reported on children's symptoms of socially withdrawn behaviors. Results showed that general patterns of attention biases were mostly the same across samples, reflecting heightened attention toward emotional versus neutral faces. The differences across two samples mostly involved the magnitude of attention biases. NBCS children were slower to disengage from happy faces when these emotional faces were paired with neutral faces. Additionally, socially withdrawn children in the NBCS sample showed a pattern of attentional avoidance for emotional faces. The comparability of overall patterns of attention biases provides initial support for the cross-cultural applicability of the eye-tracking measures and demonstrates the robustness of these methods across clinical and community settings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Looking Competent Does Not Appeal to All Voters Equally: The Role of Social Class and Politicians' Facial Appearance for Voting Likelihood.

Voters generally value competence in politicians. Four studies, all conducted in Germany, show that this is especially pronounced in people of higher compared with lower social class. The first study, with a representative sample (N1 = 2239), found that the reported importance of competence in politicians increased with increasing socioeconomic status (SES). This was mediated by self-perceived competence which was higher in participants of higher SES. In three further studies (two preregistered, N2a&2b = 396, N3 = 400) participants merely saw pictures of politicians' faces. Perceived competence based on facial appearance increased the likelihood of voting for a politician. Again, this effect was stronger among participants of higher compared with lower SES. This moderation persisted after controlling for participants' political orientation and politicians' perceived warmth and dominance. We discuss implications for future research on the psychological underpinnings of social class as well as appearance effects in the political context.