Pictures Of Faces Research Articles

<p>Fear and Anger Prime Effects on Cognitive Performance: The Role of Prime Visibility</p>

Based on the Implicit-Affect-Primes-Effort (IAPE) model (Gendolla, 2012, 2015), the present experiment investigated the role of prime visibility as a moderator of fear and anger primes’ effect on cognitive performance. Previous research has revealed inconsistent effects. Participants worked on a d2 mental concentration task with integrated pictures of fearful vs. angry faces, which were presented either masked (25 ms) or clearly visible (775 ms). Cognitive performance was assessed in terms of response accuracy and reaction times. Prime visibility significantly moderated the affect primes’ effect on response accuracy: When the primes were visible, fear expressions resulted in significantly lower response accuracy than anger primes. The opposite pattern occurred when the affect primes were masked. Additionally, visible primes led to slower responses in general, suggesting controlled prime processing. The observed performance effects corroborate recent findings on physiological measures of resource mobilization in the context of the IAPE model. Highlights Participants were presented with masked (25 ms) vs. clearly visible (775 ms) primes of anger or fear during a mental concentration task. The visibility of the primes significantly moderated the effect of affect primes on response accuracy. When the primes were visible, fear primes resulted in significantly lower response accuracy than anger primes. The opposite pattern occurred when the affect primes were masked. The performance results corroborate recent physiological findings related to the IAPE model.

The effect of sad mood on early sensory event-related potentials to task-irrelevant faces

It has been shown that the perceiver’s mood affects the perception of emotional faces, but it is not known how mood affects preattentive brain responses to emotional facial expressions. To examine the question, we experimentally induced sad and neutral mood in healthy adults before presenting them with task-irrelevant pictures of faces while an electroencephalography was recorded. Sad, happy, and neutral faces were presented to the participants in an ignore oddball condition. Differential responses (emotional – neutral) for the P1, N170, and P2 amplitudes were extracted and compared between neutral and sad mood conditions. Emotional facial expressions modulated all the components, and an interaction effect of expression by mood was found for P1: an emotional modulation to happy faces, which was found in neutral mood condition, disappeared in sad mood condition. For N170 and P2, we found larger response amplitudes for both emotional faces, regardless of the mood. The results add to the previous behavioral findings showing that mood already affects low-level cortical feature encoding of task-irrelevant faces.

Modulation of amygdala activity for emotional faces due to botulinum toxin type A injections that prevent frowning

According to the facial feedback hypothesis, when we see an angry or happy face, we contract or flex the relevant muscles to recreate the expression to assist in identifying and experiencing the emotion reflected. We investigated the facial feedback hypothesis by using botulinum toxin type A (onabotulinumtoxinA; onabotA) injections to induce temporary paralysis in the glabellar muscles (responsible for frowning) and measured functional brain activity during the processing of emotional faces. Ten females viewed pictures of happy and angry faces during two functional magnetic resonance imaging (fMRI) scan sessions: one prior (Pre) to onabotA and one following (Active) onabotA injections. We found Pre vs. Active onabotA modulation of activity in the amygdala for both happy and angry faces, as well as modulation of activity in the fusiform gyrus for happy faces. Consistent with our predictions, preventing frowning through inhibition of glabellar muscle contraction altered amygdala processing for emotional faces. The modulation of amygdala and fusiform gyrus activity following onabotA may reflect compensatory processes in a neuroanatomical circuit involved in emotional processing that is engaged when facial feedback is impaired. These data contribute to a growing literature suggesting that inhibition of glabellar muscle contraction alters neural activity for emotional processing.Clinical Trials.gov registration number: NCT03373162.

Social context modulates autonomic responses to direct eye contact

Eye contact with another person (social gaze) can evoke emotions, produce autonomic arousal, and influence behavior. Gaze cues can be evocative even when presented in static pictures of faces suggesting that responses depend on low-level visual features of gaze stimuli. The current study examined whether emotional gaze responses depend on the physical stimulus properties of an eye contact experience versus the cognitive evaluation of the social context of gaze. This was done by comparing skin conductance responses (SCR), an index of emotional arousal, during episodes of social gaze and 'self-gaze' (gazing at one's own eyes in a mirror), keeping other aspects of the viewing conditions constant. We compared SCRs during social gaze and self-gaze in forty participant pairs. Each participant engaged in ten, 20 second eye contact trials, alternating between social and self-gaze. Self-gaze episodes produced significant SCRs but social gaze SCR's were larger and occurred more reliably. SCRs decreased across trials (habituation effect) in both conditions. We speculated that social gaze between opposite sex partners might yield larger SCRs but this was not found. Overall, these results conceptually replicate previous findings of (likely top-town) cognitive regulation of autonomic gaze responses based on evaluation of the social context.

Comparison of the effects of 2 frequencies of application of photobiomodulation on facial rejuvenation: Controlled, randomized, and double-blind clinical trial.

Skin aging is an irreversible, slow and progressive process, mainly influenced by age, but also by external factors such as ultraviolet radiation, smoking, and alcohol, among others. It is increasingly common to look for procedures that slow down skin aging by limiting or hiding its effects on appearance. Studies have shown the benefits of photobiomodulation (PBM) for the skin, especially with the use of red light-emitting diodes. However, there is a high level of variability in the treatment parameters and frequency of application. The objective of this study is to compare the effects of PBM with a light-emitting diode mask (660 nm, 6.4 mW/ cm², 8,02 J/ cm², 5.02 mW, 21 minutes) on facial rejuvenation using 2 frequency applications for 4 weeks: one group will receive PBM application on the face, twice a week and another group will receive PBM application 3 times a week. A group with simulated PBM applied twice a week for 4 weeks will be used as a control. The treatment will be performed on female participants aged between 45 and 60 years. After 4 weeks, evaluations of photographic images by specialists (Wrinkle Assessment Scale) as well as the quantitative analysis of the wrinkle size by the Image J software, the depth and width of wrinkles (assessment of face impressions by optical coherence tomography) and the level of Satisfaction with Facial Appearance Overall will be compared with data collected before the start of the study. All data will be analyzed statistically according to their distribution, seeking a level of statistical significance of 0.05. This protocol was approved by the Research Ethics Committee of the Nove de Julho University (acceptance number: 4.365.565). This trial has been registered in ClinicalTrials.gov (ID: NCT04911140). This study is recruiting.

Rapid and delayed stress effects on recognition of female and male faces

Stress and the stress hormone cortisol typically impair memory recognition, especially for emotional words, scenes or objects. However, prior research almost exclusively focused on rapid non-genomic cortisol effects. Additionally, findings for stress hormone effects on face stimuli are contradictory and rare, although very relevant for everyday life. In this preregistered study, we investigated the rapid and delayed stress effects on memory recognition for faces. In a two-day design, 52 healthy men first encoded pictures of male and female faces with distinct emotional expressions. One day later, participants were exposed to a psychophysiological stress (Socially Evaluated Cold-Pressor Test) or a (warm water) control procedure. Memory for the faces was tested at two time points: 25 min after stress onset at the peak of the cortisol increase for stressed participants (rapid non-genomic cortisol effects, which presumably operate within minutes through membrane bound receptors), as well as 90 min after stress onset when cortisol concentrations were back to baseline (delayed genomic cortisol effects, which describe an altered gene transcription resulting in modified neural functions, acting supposedly via intracellular receptors). Rapid stress effects led to enhanced memory recognition for female faces selectively, whereas delayed stress effects led to enhanced memory recognition across male and female faces. Altogether, we observed a beneficial rather than detrimental impact of stress on face recognition with a differential impact on recognition of male and female faces over time. It remains to be determined if this beneficial stress effect relies on the interaction of participants’ sex and the sex of facial stimuli. Future research should also more closely look at the underlying mechanisms of how stress exactly influences face recognition, which is for example critically relevant for testimonies.

Design of Boosted Cascade and RTLBP Features based Face Recognition Module Using ML in OpenCV

Over the past ten years, face recognition in images has become more and more important. Since facial recognition is a natural talent for humans, it could be challenging to train a computer to recognize faces. The detecting difficulty comprises three stages. Without the requirement for picture difference or skin color identification, the detector operates in real-time at fifteen frames per second. Proposed and developed is a face recognition system based on Real Time Local Binary Pattern (RTLBP) and Boosted Cascade of Simple Features (BCSF). Preprocessing, face identification and recognition, and annotation are the three stages of the system. Segmentation, filtering, thresholding, and improving image quality are all part of preprocessing. The face, which is crucial for identification, is identified using the BCSF approach. Every face in the database has a correlation and RTLBP calculation. Correlation and RTLBP analysis between the captured face and the database face is then used to conduct recognition. One statistical technique that falls within the large category of factor analysis is the Boosted Cascade of Simple Features. By reducing the massive quantity of data stored to the extent of the feature space needed to represent the data efficiently, the BCSF seeks to achieve this goal. The RTLBP is designed to recognize faces by using a wide 1-D pixel vector consisting of a 2-D face picture in compact main parts of the space function. This is referred to as self-space projection. The identity of the vectors in the covariance matrix, which is centred on a set of fingerprint images, helps determine the proper space. Using OpenCV, Open Computer Vision (OpenCV), Python, Eigen-face, Fisher Face, LBPH, and Haar Cascade programming, an algorithm for a camera-based, real-time face recognition system is developed in this project. The name of the detected individual appears in the top left corner of a highlighted rectangle that annotates the face. The process of recognition takes 10.18 seconds, with a 99.93% success rate.

Generality imaging for optimized face classification using deep learning techniques

Relied on discernible or corporeal attributes, human beings are recognized by employing biometric scheme. In computer perception and design ratification domain, progressive studies are carried out in face recognition. Given the constant development in the discipline of imaging sensor, a legion of rest of the novel problems has occurred. The chief issue remains how to discover focus region more precisely for multi-focus face detection. Several studies have been proliferated in face discernment, spotting, and protection acknowledgment; the key problem remains in this is considering those images into contemplation that had “disparate dimensions” and “disparate aspect ratio” in a singular frame avoiding the progression to attain or surpass human-level accuracy in human facial aspect like noise in face pictures, defying lighting conditions and posture ratio.

Impressions of Male Centripetal and Centrifugal Faces

Impressions of Male Centripetal and Centrifugal Faces

A Comprehensive Survey on Face Quality Detection in a Video Frame

The correctness of the generated face data, which is impacted by a number of variables, significantly affects how well face analysis and recognition systems perform. By automatically analysing the face data quality in terms of its biometric value, it might be able to identify low-quality data and take the necessary action. With a focus on visible wavelength face image input, this study summarises the body of research on the evaluation of face picture quality. The use of DL-based methods is unquestionably expanding, and there are major conceptual differences between them and current approaches, such as the inclusion of quality assessment in face recognition models. In addition to image selection, which is the topic of this article, face picture quality assessment can be used in a wide range of application scenarios. The requirement for comparative algorithm assessments and the difficulty of creating Deep Learning (DL) techniques that are intelligible in addition to providing accurate utility estimates are just a few of the issues and topics that remain unanswered. For each frame, the suggested method is compared to traditional facial feature extraction, and for a collection of video frames, it is compared to well-known clustering algorithms.

Surveillance System with Face Recognition Using Hog

Abstract: Due to various suspicious activity, the surveillance system continues to emerge in the technical field. Everyday security threats can have a serious impact on people's day-today activity. In this area, numerous techniques have been developed, however some issues have not yet been overcome. The research described in this white paper offers more precise video surveillance with less processing complexity. The system's most crucial component includes location, recognition and facial recognition. The technology pulls highlighted facial information from a live environment or video dataset. The currently recorded video data will then be used to extract the face and background frames. The extracted facial picture data is then compared to the database's face image. A security alarm or signal is issued to prompt the security team to take action if no data is discovered that fits the already-existing data. The suggested solution outperforms current systems in terms of accuracy, efficiency, and cost.

Learners with Low Working Memory Capacity Benefit More from the Presence of an Instructor’s Face in Video Lectures

This current study explores the influence of learners' working memory capacity (WMC) on the facilitation effect of an instructor's presence during video lectures. Sixty-four undergraduates were classified into high and low WMC groups based on their performance in an operation span task. They watched three types of video lectures on unfamiliar topics in a random order: video lectures with an instructor's voiceover but without presence (VN), video lectures with the instructor's face picture (VP), and video lectures with the same instructor talking (VV). We collected their eye movement data during the video lectures and their learning performance in the comprehension tests following each video. Two-way ANOVA and post-hoc analyses showed that the instructor's presence significantly improved comprehension performance in only the low WMC group. They allocated more attention to the instructor's face picture and talking head than the high WMC group. Our results highlight the value of the instructor's presence as a social cue in video lectures, which is particularly beneficial for learners with a low WMC.

Oral Health Barriers for African American Caregivers of Autistic Children.

The most persistent oral health disparities in the United States impact children from racial and ethnic minoritized groups and children diagnosed as autistic. This paper aims to describe barriers to oral care as depicted by Black/African American (B/AA) parents of autistic children to further explore how and why oral health disparities persist in this population. A purposeful sample of eleven caregivers of autistic children, ages 4 to 14 years, who identified as B/AA were interviewed twice for approximately 60-90 min each. Thematic analysis utilizing a narrative approach was employed. Three themes emerged from the data concerning the barriers that affect oral health experiences: (a) difficulty in maintaining good oral health practices, (b) challenges with access to care and resources, and (c) poor patient-provider relationships. Due to the limited research that examines the intersection of autism, B/AA culture, and oral health practices, this study provides a rich picture of the barriers families face when obtaining oral care. Many families raised issues that other parents of autistic children also identified. B/AA caregivers have demonstrated that despite their own negative dental experiences, they understand the value of good oral care practices and are willing to pursue oral care for their children.

Face pareidolia in male schizophrenia

Faces are valuable signals for efficient social interaction. Yet, social cognition including the sensitivity to a coarse face scheme may be deviant in schizophrenia (SZ). Tuning to faces in non-face images such as shadows, grilled toasts, or ink blots is termed face pareidolia. This phenomenon is poorly investigated in SZ. Here face tuning was assessed in 44 male participants with SZ and person-by-person matched controls by using recently created Face-n-Thing images (photographs of non-face objects to a varying degree resembling a face). The advantage of these images is that single components do not automatically trigger face processing. Participants were administered a set of images with upright and inverted (180° in the image plane) orientation. In a two-alternative forced-choice paradigm, they had to indicate whether an image resembled a face. The findings showed that: (i) With upright orientation, SZ patients exhibited deficits in face tuning: they provided much fewer face responses than controls. (ii) Inversion generally hindered face pareidolia. However, while in neurotypical males, inversion led to a drastic drop in face impression, in SZ, the impact of orientation was reduced. (iii) Finally, in accord with the signal detection theory analysis, the sensitivity index (d-prime) was lower in SZ, whereas no difference occurred in decision criterion. The outcome suggests altered face pareidolia in SZ is caused by lower face sensitivity rather than by alterations in cognitive bias. Comparison of these findings with earlier evidence confirms that tuning to social signals is lower in SZ, and warrants tailored brain imaging research.

First impressions from faces in the real world: Commentary on Sutherland and Young (2022).

The study of first impressions from faces now emphasizes the need to understand trait inferences made to naturalistic face images (British Journal of Psychology, 113, 2022, 1056). Face recognition algorithms based on deep convolutional neural networks simultaneously represent invariant, changeable and environmental variables in face images. Therefore, we suggest them as a comprehensive 'face space' model of first impressions of naturalistic faces. We also suggest that to understand trait inferences in the real world, a logical next step is to consider trait inferences made to whole people (faces and bodies). On the role of cultural contributions to trait perception, we think it is important for the field to begin to consider the way in which trait inferences motivate (or not) behaviour in independent and interdependent cultures.

High-frequency transcranial random noise stimulation enhances unfamiliar face matching of high resolution and pixelated faces

Face identification is useful for social interactions and its impairment can lead to severe social and mental problems. This ability is also remarkably important in applied settings, including eyewitness identification and ID verification. Several studies have demonstrated the potential of Transcranial Random Noise Stimulation (tRNS) to enhance different cognitive skills. However, research has produced inconclusive results about the effectiveness of tRNS to improve face identification. The present study aims to further explore the effect of tRNS on face identification using an unfamiliar face matching task. Observers firstly received either high-frequency bilateral tRNS or sham stimulation for 20 min. The stimulation targeted occipitotemporal areas, which have been previously involved in face processing. In a subsequent stage, observers were asked to perform an unfamiliar face matching task consisting of unaltered and pixelated face pictures. Compared to the sham stimulation group, the high-frequency tRNS group showed better unfamiliar face matching performance with both unaltered and pixelated faces. Our results show that a single high-frequency tRNS session might suffice to improve face identification abilities. These results have important consequences for the treatment of face recognition disorders, and potential applications in those scenarios whereby the identification of faces is primordial.

A facial expression recognition method based on Convolutional Neural Network

This project is the implementation method of a simple static facial expression recognition (FER) project. Under the environment of Python3, the deep learning model is used to compare with the traditional model. Finally, CNN (Convolutional Neural Networks) is actually used to construct the entire system, and model evaluation is carried out on the three FER datasets FER2013, JAFFE and CK+. The project's main functions include "get a picture of a person's face" and "recognize expressions". The principles of the above functions include the following: the establishment of neural network structure, the acquisition of data sets, the model training based on data sets, the use of OpenCV to obtain the face, and the use of the model to recognize the expression. The experimental results reproduce the simple deep learning model to realize FER and verify the different effects of different data sets on the results. Face recognition has been widely used in all aspects of life, but different purposes need different models and data collection methods, and the laboratory collection cost is high, and the amount of data is limited, so this project also discusses the data set selection methods under different purposes.

DEEP TEXTURE FEATURES FOR ROBUST FACE SPOOFING DETECTION

Biometrics arose as a vigorous answer for security frameworks. Notwithstanding, given the spread of biometric applications, lawbreakers are creating procedures to evade them by reenacting physical or social characteristics of legitimate clients (caricaturing assaults). In spite of face being a promising trademark because of its comprehensiveness, worthiness and presence of cameras all over the place, face acknowledgment frameworks are very helpless against such cheats since they can be effectively messed with normal printed facial photos. Cutting edge draws near, in view of Convolutional Brain Organizations (CNNs), present great outcomes in face ridiculing discovery. Nonetheless, these techniques don't consider the significance of advancing profound nearby elements from every facial locale, despite the fact that it is known from face acknowledgment that every facial area presents different visual angles, which can likewise be taken advantage of for face satirizing location. In this work we propose a clever CNN design prepared in two stages for such undertaking. At first, each piece of the brain network gains highlights from a given facial district. Subsequently, the entire model is adjusted overall facial pictures. Results show that such pre-preparing step permits the CNN to learn different nearby caricaturing signals, working on the exhibition and the assembly speed of the last model, beating the cutting edge draws near.

A Real-Time Visage Expression Detection Using Convolutional Neural Network (RTVED)

In this work, the purpose is to implement parts of seven Real-Time Visage Expression Detection using Convolutional Neural network (RTVED), Data Augmentation to get high accuracy, and focus on the Methodology of Real-Time Visage Expression Detection using CNN. In this paper, considered a convention Convolutional Neural Network model and utilized it to prepare and test diverse look pictures with the Keras, TensorFlow, and deep learning library. Real-Time human visage Expressions Detection has two sections, recognizer Validation, and data preparation, and a training model for data preparation and training. The recognizer contains a visage Expression detector and a visage Expression recognizer. The visage expression detector extricates facial pictures from the camera and the visage Expression recognizer recognizes the extracted pictures. The Data Training model utilizes the Convolutional Neural Network to prepare data and the recognizer likewise utilizes Convolutional Neural Network to identify the emotional condition through their visage Expressions. The framework perceives the six widespread emotions as anger, disgust, happiness, sadness, surprise neutral, and contempt.

Lost + Found: The Lost Angel Investigator

Each year, a large number of youngsters are found missing in India. Among them, a large number of cases are never solved due to various difficulties faced by the police ranging from heavy paperwork to lacking technology. Therefore, one of this work’s key goals is to provide an application that may assist people whose children have been missing and rescued by the public. This will also reduce the time required to find the missing child to reunite the child with their loved ones as soon as possible. The pictures of child victims can be uploaded by the citizens along with landmarks, to our web app. The photographs will be matched to the missing child’s registered photographs if existing in the database. A deep neural network model is trained to locate the lost youngster using a facial picture uploaded by the citizens. Multi-Tasking CNN (MTCNN), the most efficient DNN technique for image-based apps, is used for facial Identification. The images were passed through an augmentation layer to get images of different orientations, brightness, and contrast, which were used ahead to train the EfficientNetB0 model. This model is then used to recognize faces in photographs. Using the MTCNN model for facial recognition with EfficientNetB0 and developing it yields a deep learning model that is free from all types of distortion. The model’s training accuracy is 96.66 percent and its testing accuracy is 76.81 percent, implying that there is approximately 77 percent possibility of finding a match for the missing kid. It was evaluated using 25 Child classes. Each Child class has around 15 to 20 images. These images are taken with different backgrounds and real-time settings so that model will work even when noise is present in the image.