Pictures Of Faces Research Articles

How trait impressions of faces shape subsequent mental state inferences.

People form impressions of one another in a split second from faces. However, people also infer others' momentary mental states on the basis of context-for example, one might infer that somebody feels encouraged from the fact that they are receiving constructive feedback. How do trait judgements of faces influence these context-based mental state inferences? In this Registered Report, we asked participants to infer the mental states of unfamiliar people, identified by their neutral faces, under specific contexts. To increase generalizability, we representatively sampled all stimuli from inclusive sets using computational methods. We tested four hypotheses: that trait impressions of faces (1) are correlated with subsequent mental state inferences in a range of contexts, (2) alter the dimensional space that underlies mental state inferences, (3) are associated with specific mental state dimensions in this space and (4) causally influence mental state inferences. We found evidence in support of all hypotheses.

Lip Augmentation with Soft Tissue Fillers: Social Media, Perceptual Adaptation, and Shifting Beauty Trends beyond Golden Standard Ideals.

Unfortunately, current beauty trends greatly advertised by social media are trespassing recognized cannons of beauty. Of particular interest are patients' expectations of soft tissue volume augmentation of the face, specifically of the lips. An artificial intelligence picture of a White female face was generated. With the fixed standard golden upper-to-lower (U/L) lip ratio, a first set of pictures was generated with varying lip fullness. A second set of pictures was generated with various U/L ratios and lip fullness. In a survey format, the 2 sets of pictures were rated for attractiveness from the most to the least attractive by adult men and women. Rating the first set of pictures clearly demonstrates that overinflation of lips with upper lip height in excess of 30% of hemi-lip width is highly regarded as unaesthetic and unattractive, with a net preference of 20%-25%. For the second set, U/L ratios between 0.618:1 and 1:1 are still regarded to be most aesthetic. Though social media have an influence on perception of beauty, data provided by the survey confirm that golden standards of aesthetics remain constant and that exaggerated requests of some patients may not be attributed solely to social media influence but probably to social media in combination with additional personal predisposing factors. Patients must be made aware that how they are perceived by others is essential for them to be satisfied with their appearance.

Generative neural networks for experimental manipulation: Examining dominance‐trustworthiness face impressions with data‐efficient models

AbstractAn important development in the study of face impressions was the introduction of dominance and trustworthiness as the primary and potentially orthogonal traits judged from faces. We test competing predictions of recent accounts that address evidence against the independence of these judgements. To this end we develop a version of recent ‘deep models of face impressions’ better suited for data‐efficient experimental manipulation. In Study 1 (N = 128) we build impression models using 15 times less ratings per dimension than previously assumed necessary. In Study 2 (N = 234) we show how our method can precisely manipulate dominance and trustworthiness impressions of face photographs and observe how the effects' pattern of the cues of one trait on impressions of the other differs from previous accounts. We propose an altered account that stresses how a successful execution of the two judgements' functional roles requires impressions of trustworthiness and dominance to be based on cues of both traits. Finally we show our manipulation resulted in larger effect sizes using a broader array of features than previous methods. Our approach lets researchers manipulate face stimuli for various face perception studies and investigate new dimensions with minimal data collection.

Weakly Supervised 3D Face Reconstruction with Joint Spatial and Frequency Domain Information

3D face reconstruction is an important research direction in computer vision, and its goal is to recover a 3D face model from a single face picture. In the absence of real 3D face data, how to reconstruct a 3D face with a high degree of realism has become a hot research topic in recent years. Existing reconstruction algorithms usually rely on 3D labels generated from a large number of 2D face images as training data, however, inaccurate data will seriously affect the reconstruction quality. For this reason, the paper proposes a joint spatial-frequency domain decoupled weak supervision to achieve 3D face reconstruction, the main idea of which is to construct a multi-level loss function by using the weakly supervised information extracted from the spatial domain, and separating the frequency-domain information between the input and the rendered image in the frequency domain, and minimizing the difference between the two by difference computation. The method combines deep learning with 3D deformable models to reconstruct 3D models with high quality texture and shape from only a single face image. Quantitative experiments on the AFLW2000-3D and MICC Florence datasets show that the normalized average error in the small pose interval is as low as 2.42%, and the face reconstruction accuracy in the outdoor scene is 0.98 0.22 mm. Qualitative experiments on the MoFa-test, MICA datasets show that when faced with reconstruction with different poses, lighting, and expressions , our method outperforms other state-of-the-art reconstruction methods.

Automatic identification of breech face impressions based on deep local features

Breech face impressions are an essential type of physical evidence in forensic investigations. However, their surface morphology is complex and varies based on the machining method used on the gun’s breech face, making traditional handcrafted local feature-based methods exhibit high false rates and are unsuitable for striated impressions. We proposed a deep local feature-based method for firearm identification utilizing Detector-Free Local Feature Matching with Transformers (LoFTR). This method removes the module of feature point detection and directly utilizes self and cross-attention layers in the Transformer to transform the convolved coarse-level feature maps into a series of dense feature descriptors. Subsequently, matches with high confidence scores are filtered based on the score matrix calculated from the dense descriptors. Finally, the screened initial matches are refined into the convolved fine-level features, and a correlation-based approach is used to obtain the exact location of the match. Validation tests were conducted using three authoritative sets of the breech face impressions datasets provided by the National Institute of Standards and Technology (NIST). The validation results show that, compared with the traditional handcrafted local-feature based methods, the proposed method in this paper yields a lower identification error rate. Notably, the method can not only deal with granular impressions, but can also be applied to the striated impressions. The results indicate that the method proposed in this paper can be utilized for comparative analysis of breech face impressions, and provide a new automatic identification method for forensic investigations.

A Multidimensional Neural Representation of Face Impressions.

From a glimpse of a face, people form trait impressions that operate as facial stereotypes, which are largely inaccurate yet nevertheless drive social behavior. Behavioral studies have long pointed to dimensions of trustworthiness and dominance that are thought to underlie face impressions due to their evolutionarily adaptive nature. Using human neuroimaging (N = 26, 19 female, 7 male), we identify a two-dimensional representation of faces' inferred traits in the middle temporal gyrus (MTG), a region involved in domain-general conceptual processing including the activation of social concepts. The similarity of neural-response patterns for any given pair of faces in the bilateral MTG was predicted by their proximity in trustworthiness-dominance space, an effect that could not be explained by mere visual similarity. This MTG trait-space representation occurred automatically, was relatively invariant across participants, and did not depend on the explicit endorsement of face impressions (i.e., beliefs that face impressions are valid and accurate). In contrast, regions involved in high-level social reasoning (the bilateral temporoparietal junction and posterior superior temporal sulcus; TPJ-pSTS) and entity-specific social knowledge (the left anterior temporal lobe; ATL) also exhibited this trait-space representation but only among participants who explicitly endorsed forming these impressions. Together, the findings identify a two-dimensional neural representation of face impressions and suggest that multiple implicit and explicit mechanisms give rise to biases based on facial appearance. While the MTG implicitly represents a multidimensional trait space for faces, the TPJ-pSTS and ATL are involved in the explicit application of this trait space for social evaluation and behavior.

Modulation of the attentional response to baby schema by testosterone in pedohebephilic men and its relation to the nurturing system

Previous fMRI research found increased brain responses in men with pedophilic interest to non-sexual pictures of child and animal faces. This raised the question of whether an aberrant nurturing system could be linked to pedophilia. To further explore this hypothesis, 20 pedohebephilic and 23 teleiophilic men performed a target detection task with adult versus infant human and animal faces, which measured selective attention towards the baby schema by comparing reaction times to infant versus adult targets that were presented amongst distractors of the other category. Since the response to baby schema can be influenced by steroid hormones, saliva samples were additionally collected to determine endogenous testosterone, progesterone, estradiol and cortisol. Contrary to expectations, all men did not react faster to infant than adult faces. Yet, pedohebephilic men were more distracted by infant’s faces than teleiophilic men. Pedohebephilic men with higher testosterone were faster in orienting attention to infant targets in the context of adult distractors. This association was not observed in teleiophilic men. Our results support the idea of an overactive nurturing system in pedophilia, which may be influenced by the endogenous testosterone level.

Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are extensively being harnessed across a diverse range of domains, e.g., forensic science, healthcare, virtual assistants, cybersecurity, and robotics. On the flip side, they can also be exploited for negative purposes, like producing authentic-looking fake news that propagates misinformation and diminishes public trust. Deepfakes pertain to audio or visual multimedia contents that have been artificially synthesized or digitally modified through the application of deep neural networks. Deepfakes can be employed for benign purposes (e.g., refinement of face pictures for optimal magazine cover quality) or malicious intentions (e.g., superimposing faces onto explicit image/video to harm individuals producing fake audio recordings of public figures making inflammatory statements to damage their reputation). With mobile devices and user-friendly audio and visual editing tools at hand, even non-experts can effortlessly craft intricate deepfakes and digitally altered audio and facial features. This presents challenges to contemporary computer forensic tools and human examiners, including common individuals and digital forensic investigators. There is a perpetual battle between attackers armed with deepfake generators and defenders utilizing deepfake detectors. This paper first comprehensively reviews existing image, video, and audio deepfake databases with the aim of propelling next-generation deepfake detectors for enhanced accuracy, generalization, robustness, and explainability. Then, the paper delves deeply into open challenges and potential avenues for research in the audio and video deepfake generation and mitigation field. The aspiration for this article is to complement prior studies and assist newcomers, researchers, engineers, and practitioners in gaining a deeper understanding and in the development of innovative deepfake technologies.

Trustworthiness judgments and pupil-size in individuals with schizophrenia

Individuals with schizophrenia show aberrant processing of social cues. In the current study, we (1) compared trustworthiness ratings of faces between patients with schizophrenia and healthy controls, (2) compared pupillary reactivity between patients and controls (3) examined whether trustworthiness judgments in schizophrenia are related to pupil reactivity, (4) and examined associations between trustworthiness judgements and symptom severity, specifically paranoia. Patients with schizophrenia spectrum disorders (N = 48) and healthy controls (N = 33) completed a Trustworthiness Task, during which their pupil size was measured via an eye-tracking device. The mean baseline-corrected pupil size was calculated from 24 pictures of real neutral faces, each presented for 2500 ms. Self-reported psychotic experiences were measured by Community Assessment of Psychic Functioning (CAPE–42), and symptom severity was rated by Brief Psychiatric Rating Scale (BPRS). No group differences were found in trustworthiness ratings or pupil reactivity parameters during trustworthiness judgments. Separately, among patients, absolute difference in pupil-size change and dilation after reaching minimum size were related to more severe positive symptoms and self-reported paranoia. Our results did not show social cognitive biases in the stable outpatients with schizophrenia, or the role of pupil reactivity in trustworthiness judgments. Future studies should use longer stimuli for pupillary reactivity and control the type and dosage of utilized antipsychotic medication. Further studies are required to explore relationships in larger and more symptomatic groups of patients.

Decoding the language of first impressions: Comparing models of first impressions of faces derived from free-text descriptions and trait ratings.

First impressions formed from facial appearance predict important social outcomes. Existing models of these impressions indicate they are underpinned by dimensions of Valence and Dominance, and are typically derived by applying data reduction methods to explicit ratings of faces for a range of traits. However, this approach is potentially problematic because the trait ratings may not fully capture the dimensions on which people spontaneously assess faces. Here, we used natural language processing to extract 'topics' directly from participants' free-text descriptions (i.e., their first impressions) of 2222 face images. Two topics emerged, reflecting first impressions related to positive emotional valence and warmth (Topic 1) and negative emotional valence and potential threat (Topic 2). Next, we investigated how these topics were related to Valence and Dominance components derived from explicit trait ratings. Collectively, these components explained only ~44% of the variance in the topics extracted from free-text descriptions and suggested that first impressions are underpinned by correlated valence dimensions that subsume the content of existing trait-rating-based models. Natural language offers a promising new avenue for understanding social cognition, and future work can examine the predictive utility of natural language and traditional data-driven models for impressions in varying social contexts.

Le port du masque affecte l'identification des expressions faciales émotionnelles, surtout chez les personnes âgées

Younger adults have difficulties identifying emotional facial expressions from faces covered by face masks. It is important to evaluate how face mask wearing might specifically impact older people, because they have lower emotion identification performance than younger adults, even without face masks. We compared performance of 62young and 38older adults in an online task of emotional facial expression identification using masked or unmasked pictures of faces with fear, happiness, anger, surprise, and neutral expression, from different viewpoints. Face masks affected performance in both age groups, but more so in older adults, specifically for negative emotions (anger, fear), in favour of the saliency hypothesis as an explanation for the positive advantage. Additionally, face masks more affected emotion recognition on profile than on three-quarter or full-face views. Our results encourage using clearer and full-face expressions when dealing with older people while wearing face masks.

REAL TIME FACE ATTENDANCE SYSTEM USING DEEP LEARNING

One's face is a physical depiction of who they are. As a result, we have suggested a facial recognition- based automatic student attendance system. The usage of face recognition technology in everyday life is particularly beneficial for security control systems. Face recognition is used by the FBI (Federal Bureau of Investigation) for criminal investigations as well as the airport security system to identify criminals. In the first step of our suggested method, video framing, the camera is activated using a user-friendly interface. The Viola-Jones method is used to identify and segment the face ROI from the video frame. If necessary, image size scaling is done at the pre-processing step to prevent information loss. After using the median filter to reduce noise, color pictures are transformed into grayscale versions. Then, to improve the contrast of the pictures, contrast-limited adaptive histogram equalization (CLAHE) is applied. Principal component analysis (PCA) and improved local binary pattern (LBP) are used appropriately in the face identification stage to extract the characteristics from facial pictures. By minimizing the lighting impact and boosting identification rates, the improved local binary pattern outperforms the original local binary pattern in our suggested method. The features that were retrieved from the training pictures and the features that were extracted from the test images are then compared. The face pictures are then identified and categorized using the upgraded LBP, PCA, and algorithm that produced the best results. The acknowledged student's attendance will then be recorded and saved in the excel file. A warning will be sent if a student signs in more than once. The student who is not enrolled will also be allowed to register right away. When two photographs per person are trained, the average recognition accuracy is 100% for high-quality images, 94.12% for low-quality images, and 95.76% for the face database.

Humanitarian Facial Recognition for Rare Craniofacial Malformations.

Children with congenital disorders are unfortunate collateral victims of wars and natural disasters. Improved diagnosis could help organize targeted medical support campaigns. Patient identification is a key issue in the management of life-threatening conditions in extreme situations, such as in oncology or for diabetes, and can be challenging when diagnosis requires biological or radiological investigations. Dysmorphology is a central element of diagnosis for craniofacial malformations, with high sensibility and specificity. Massive amounts of public data, including facial pictures circulate daily on news channels and social media, offering unique possibilities for automatic diagnosis based on facial recognition. Furthermore, AI-based algorithms assessing facial features are currently being developed to decrease diagnostic delays. Here, as a case study, we used a facial recognition algorithm trained on a large photographic database to assess an online picture of a family of refugees. Our aim was to evaluate the relevance of using an academic tool on a journalistic picture and discuss its potential application to large-scale screening in humanitarian perspectives. This group picture featured one child with signs of Apert syndrome, a rare condition with risks of severe complications in cases of delayed management. We report the successful automatic screening of Apert syndrome on this low-resolution picture, suggesting that AI-based facial recognition could be used on public data in crisis conditions to localize at-risk patients.

Research and Analysis on the Characteristics of College Students' Emotional Face Processing Based on Computer Vision Technology

The processing of emotional faces by individuals is an important basis for the perception and understanding of emotions. In accordance with the score of Schutte self-report Emotional Intelligence Scale (SSEIS), students in high and low emotional intelligence groups were screened out. Based on the computer vision technology, four kinds of emotional face pictures, including pleasure, anger, sadness and fear, were randomly presented with the help of the eye tracker, and four interest areas were divided into eyebrows, eyes, nose and mouth. The eye-movement indexes of subjects were recorded, and common characteristics of emotional face processing of college students and differences in gender and emotional intelligence were explored. The results showed that college students had a positive bias to emotional face processing and a certain bias to eye area; the number of fixation points in high emotional intelligence group was more and concentrated, and the processing was more rapid and efficient, while the number of fixation points in low emotional intelligence group was less and scattered, and the processing efficiency was lower; female college students were more detailed in emotional face processing.

Facial soft-biometrics obfuscation through adversarial attacks

Sharing facial pictures through online services, especially on social networks, has become a common habit for thousands of users. This practice hides a possible threat to privacy: the owners of such services, as well as malicious users, could automatically extract information from faces using modern and effective neural networks. In this paper, we propose the harmless use of adversarial attacks, i.e. variations of images that are almost imperceptible to the human eye and that are typically generated with the malicious purpose to mislead Convolutional Neural Networks (CNNs). Such attacks have been instead adopted to (i) obfuscate soft biometrics (gender, age, ethnicity) but (ii) without degrading the quality of the face images posted online. We achieve the above mentioned two conflicting goals by modifying the implementations of four of the most popular adversarial attacks, namely FGSM, PGD, DeepFool and C&W, in order to constrain the average amount of noise they generate on the image and the maximum perturbation they add on the single pixel. We demonstrate, in an experimental framework including three popular CNNs, namely VGG16, SENet and MobileNetV3, that the considered obfuscation method, which requires at most four seconds for each image, is effective not only when we have a complete knowledge of the neural network that extracts the soft biometrics (white box attacks), but also when the adversarial attacks are generated in a more realistic black box scenario. Finally, we prove that an opponent can implement defense techniques to partially reduce the effect of the obfuscation, but substantially paying in terms of accuracy over clean images; this result, confirmed by the experiments carried out with three popular defense methods, namely adversarial training, denoising autoencoder and Kullback-Leibler autoencoder, shows that it is not convenient for the opponent to defend himself and that the proposed approach is robust to defenses.

Semantic differences in visually similar face emojis

Abstract The literature on face emojis raises the central question whether they should be treated as pictures or conventionalized signals. Our experiment addresses this question by investigating semantic differences in visually similar face emojis. We test a prediction following from a pictorial approach: small visual features of emojis that do not correspond to human facial features should be semantically less relevant than features that represent aspects of facial expressions. We compare emoji pairs with a visual difference that either does or does not correspond to a difference in a human facial expression according to an adaptation of the Facial Action Coding System. We created two contexts per pair, each fitted to correspond to a prominent meaning of one or the other emoji. Participants had to choose a suitable emoji for each context. The rate at which the context-matching emoji was chosen was significantly above chance for both types of emoji pairs and it did not differ significantly between them. Our results show that the small differences are meaningful in all pairs whether or not they correspond to human facial differences. This supports a lexicalist approach to emoji semantics, which treats face emojis as conventionalized signals rather than mere pictures of faces.

Metacognitive accuracy predicts self-reported quality of life following traumatic brain injury

ABSTRACT Objective Metacognition and quality of life (QoL) are both adversely affected by traumatic brain injury (TBI), but the relation between them is not fully understood. As such, the purpose of this study was to determine the degree to which metacognitive accuracy predicts QoL in individuals with TBI. Methods Eighteen participants with moderate-to-severe TBI completed a stimulus-response task requiring the discrimination of emotions depicted in pictures of faces and then provided a retrospective confidence judgment after each response. Metacognitive accuracy was calculated using participants’ response accuracy and confidence judgment accuracy. Participants also completed the Quality of Life After Brain Injury (QOLIBRI) questionnaire to assess QoL in various areas of functioning. Results Performance of a linear regression analysis revealed that higher metacognitive accuracy significantly predicted lower overall QoL. Additionally, higher metacognitive accuracy significantly predicted lower QoL related to cognition and physical limitations. Conclusion The study results provide evidence of an inverse relation between metacognitive performance and QoL following TBI. Metacognitive changes associated with TBI and their relation to QoL have several clinical implications for TBI rehabilitation.

See no evil: Attentional bias toward threat is diminished in aged monkeys.

Prior evidence demonstrates that relative to younger adults, older human adults exhibit attentional biases toward positive and/or away from negative socioaffective stimuli (i.e., the age-related positivity effect). Whether or not the effect is phylogenetically conserved is currently unknown and its biopsychosocial origins are debated. To address this gap, we evaluated how visual processing of socioaffective stimuli differs in aged, compared to middle-aged, rhesus monkeys (Macaca mulatta) using eye tracking in two experimental designs that are directly comparable to those historically used for evaluating attentional biases in humans. Results of our study demonstrate that while younger rhesus possesses robust attentional biases toward threatening pictures of conspecifics' faces, aged animals evidence no such bias. Critically, these biases emerged only when threatening faces were paired with neutral and not ostensibly "positive" faces, suggesting social context modifies the effect. Results of our study suggest that the evolutionarily shared mechanisms drive age-related decline in visual biases toward negative stimuli in aging across primate species. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

<p>In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.</p>

Leveraging a Two-Level Attention Mechanism for Deep Face Recognition with Siamese One-Shot Learning

Discriminative feature embedding is used for largescale facial recognition. Many image-based facial recognition networks use CNNs like ResNets and VGG-nets. Humans prioritise different elements, but CNNs treat all facial pictures equally. NLP and computer vision use attention to learn the most important part of an input signal. The inter-channel and inter-spatial attention mechanism is used to assess face image component significance in this study. Channel scalars are calculated using Global Average Pooling in face recognition channel attention. A recent study found that GAP encodes low-frequency channel information first. We compressed channels using discrete cosine transform (DCT) instead of scalar representation to evaluate information at frequencies other than the lowest frequency for the channel attention mechanism. Later layers can acquire the feature map after spatial attention. Channel and spatial attention increase CNN facial recognition feature extraction. Channel-only, spatial-only, parallel, sequential, or channel-after-spatial attention blocks exist. Current face recognition attention approaches may be outperformed on public datasets (Labelled Faces in the Wild).