Importance Of Facial Expressions Research Articles

Sighted People Recognizing Emotions in Facial Expression Images of People With Visual Disabilities Via Cyberspace

As technology advances today, there are many opportunities for people to communicate in cyberspace (e.g., videoconferencing) regardless of visual ability/disability. People share various information including facial expressions that are considered essential for social interaction. Despite the importance of facial expressions in conveying emotions, there is limited knowledge on how sighted people perceive the emotions of people with visual disabilities from their facial expressions online. To address the knowledge gap, this study showed sighted participants facial expression images of people with visual disabilities via Zoom and asked them to report the emotion they perceived for each image. Afterward, participants were educated about the facial modeling from a previous study and shown another set of facial expression images of people with visual disabilities. Participants were asked to report the emotion they perceived in each image. This study found many cases of incongruently perceived emotions, in which participants perceived emotions differently from the facially expressed emotions of people with visual disabilities. Yet, after education, participants were more likely to congruently perceive emotions. The research findings would be beneficial to many stakeholders in the domain of inclusive communication for everyone in cyberspace.

Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10)

The importance of facial expressions in nonverbal communication is significant because they help better represent the inner emotions of individuals. Emotions can depict the state of health and internal wellbeing of individuals. Facial expression detection has been a hot research topic in the last couple of years. The motivation for applying the convolutional neural network-10 (CNN-10) model for facial expression recognition stems from its ability to detect spatial features, manage translation invariance, understand expressive feature representations, gather global context, and achieve scalability, adaptability, and interoperability with transfer learning methods. This model offers a powerful instrument for reliably detecting and comprehending facial expressions, supporting usage in recognition of emotions, interaction between humans and computers, cognitive computing, and other areas. Earlier studies have developed different deep learning architectures to offer solutions to the challenge of facial expression recognition. Many of these studies have good performance on datasets of images taken under controlled conditions, but they fall short on more difficult datasets with more image diversity and incomplete faces. This paper applied CNN-10 and ViT models for facial emotion classification. The performance of the proposed models was compared with that of VGG19 and INCEPTIONV3. The CNN-10 outperformed the other models on the CK + dataset with a 99.9% accuracy score, FER-2013 with an accuracy of 84.3%, and JAFFE with an accuracy of 95.4%.

Facial Expressions Increase Emotion Recognition Clarity and Improve Warmth and Attractiveness on a Humanoid Robot without Adding the Uncanny Valley

With the rising impact of social robots, research on understanding user perceptions and preferences of these robots gains further importance, especially for emotional expressions. However, research on facial expressions and their effects on human-robot interaction has had mixed results. To unpack this issue further, we investigated users’ emotion recognition accuracy and perceptions when interacting with a social robot that displayed emotional facial expressions or not in a storytelling setting. In our experiment, twenty-eight participants received verbal feedback either with or without facial expressions from the robot. Participants showed a significant recognition accuracy effect for emotions and significantly higher clarity for disgust, happiness, and surprise in the facial expression condition than in the no facial expression condition. In addition, participants rated Milo with facial expressions significantly higher than Milo without facial expressions in warmth and attractiveness. No significant differences were found among the rating scores of naturalness. The results from the present study indicated the importance of facial expressions when considering design choices in social robots.

ISIEA: An image database of social inclusion and exclusion in young Asian adults

Human beings have a fundamental need to belong. Evaluating and dealing with social exclusion and social inclusion events, which represent negative and positive social interactions, respectively, are closely linked to our physical and mental health. In addition to traditional paradigms that simulate scenarios of social interaction, images are utilized as effective visual stimuli for research on socio-emotional processing and regulation. Since the current mainstream emotional image database lacks social stimuli based on a specific social context, we introduced an open-access image database of social inclusion/exclusion in young Asian adults (ISIEA). This database contains a set of 164 images depicting social interaction scenarios under three categories of social contexts (social exclusion, social neutral, and social inclusion). All images were normatively rated on valence, arousal, inclusion score, and vicarious feeling by 150 participants in Study 1. We additionally examined the relationships between image ratings and the potential factors influencing ratings. The importance of facial expression and social context in the image rating of ISIEA was examined in Study 2. We believe that this database allows researchers to select appropriate materials for socially related studies and to flexibly conduct experimental control.

Reconhecimento de emoções em rostos e comportamento ocular na ansiedade social: Um estudo sobre eye-tracking

Eye-tracking is a tracking system through which one can study the emotional recognition of subjects diagnosed with social anxiety. Given the importance of facial expressions and their recognition, this study aimed to investigate the recognition of emotions in faces as well as eye movements observed in subjects with social anxiety. For that purpose, eye-tracking technology was used to understand whether individuals with high anxiety revealed different attentional patterns than subjects with low or no social anxiety. For this purpose, a systematic review was carried out, resulting in 20 articles. With regards to the main results, it was found that ocular patterns are concentrated mainly in the ocular region when participants are in the presence of emotions considered negative. In contrast, in the presence of positive emotions, eye movements were directed towards the mouth region were observed. The literature suggests initial hypervigilance in attentional processes effect and later avoidance, especially when facing negative facial expressions such as anger. However, the present review showed disagreement between the researchers. It is concluded that, in general, eye-tracking systems are advantageous, with applicability in subclinical and clinical populations, such as social anxiety.

Deep learning for the recognition of facial expression in the Colombian sign language

The communication of the deaf person has been considered as a set of manual movements, however many have ignored the importance of facial expression in the different communicative manifestations of this population; this is the reason because a group of Phonoaudiology, physiotherapy and engineering researchers integrate their knowledge to perform image processing and classification of facial expressions used in the Colombian Sign Language (LSC) analysis. The Objective was to establish the processing of images of facial expressions as a complementary means to manual movements for the interpretation of Colombian sign language. Qualitative study, descriptive, non-experimental design, for which four phases were considered, in the first, data collection was carried out through recordings of deaf people as linguistic models producing facial expressions corresponding to the vocabulary of the clinical scenario. In phase two, the images are processed to identify the characteristic patterns of each sign. In the third phase, two Deep Learning techniques are used to classify the captured gestures. In the fourth phase, the accuracy of the images was validated techniques used. For the classification process, six facial expressions corresponding to the words pain, inflammation, fracture, irritable colon, dizziness, diabetes were analyzed. In this process, two Deep Learning techniques were validated, obtaining that the Single Shot Multibox Detector SSD technique has a precision of 94, 2% compared to the Convolutional Neural Network (CNN) technique, which has a degree of accuracy of 89.05%. The development of technologies of this nature allows analyzing the facial expression in the communication of the deaf person as a distinctive feature for interaction with others. The algorithms of artificial vision based on Deep learning present a high level of efficiency in the classification of facial expression, this being an important factor to generate tools that facilitate the communication of deaf and hearing.

Analyses of a Multimodal Spontaneous Facial Expression Database

Creating a large and natural facial expression database is a prerequisite for facial expression analysis and classification. It is, however, not only time consuming but also difficult to capture an adequately large number of spontaneous facial expression images and their meanings because no standard, uniform, and exact measurements are available for database collection and annotation. Thus, comprehensive first-hand data analyses of a spontaneous expression database may provide insight for future research on database construction, expression recognition, and emotion inference. This paper presents our analyses of a multimodal spontaneous facial expression database of natural visible and infrared facial expressions (NVIE). First, the effectiveness of emotion-eliciting videos in the database collection is analyzed with the mean and variance of the subjects' self-reported data. Second, an interrater reliability analysis of raters' subjective evaluations for apex expression images and sequences is conducted using Kappa and Kendall's coefficients. Third, we propose a matching rate matrix to explore the agreements between displayed spontaneous expressions and felt affective states. Lastly, the thermal differences between the posed and spontaneous facial expressions are analyzed using a paired-samples t-test. The results of these analyses demonstrate the effectiveness of our emotion-inducing experimental design, the gender difference in emotional responses, and the coexistence of multiple emotions/expressions. Facial image sequences are more informative than apex images for both expression and emotion recognition. Labeling an expression image or sequence with multiple categories together with their intensities could be a better approach than labeling the expression image or sequence with one dominant category. The results also demonstrate both the importance of facial expressions as a means of communication to convey affective states and the diversity of the displayed manifestations of felt emotions. There are indeed some significant differences between the temperature difference data of most posed and spontaneous facial expressions, many of which are found in the forehead and cheek regions.

Teachers Perception Regarding Facial Expressions As An Effective Teaching Tool

The major objective of the study was to explore teachers perceptions about the importance of facial expression in the teaching-learning process. All the teachers of government secondary schools constituted the population of the study. A sample of 40 teachers, both male and female, in rural and urban areas of district Peshawar, were selected through a simple random sampling technique. Data were collected through a questionnaire containing ten items regarding facial expressions. It was concluded that teachers deemed the technology of facial expressions very essential and important, not only for themselves, but also for the attainment of students learning outcomes.

Interpretation of facial expressions and social anxiety: Specificity and source of biases

Cognitive-behavioural models of social phobia (Clark & Wells, 1995; Rapee & Heimberg, 1997) propose that biased information processing contributes to the maintenance of social phobia. Given the importance of facial expressions in social interactions, recent investigations of these information-processing biases have increasingly used facial stimuli. The current study utilised schematic faces of emotional expressions to investigate interpretations of facial expressions and specific facial features in individuals with high and low social anxiety. Individuals with elevated social anxiety demonstrated biases in their perceptions of negative valence from the faces, whereas group differences were not observed for perceptions of activity or potency. Further, although the two groups generally utilised the same facial features to interpret facial expressions, the results suggested that individuals with high social anxiety may be more lenient in perceiving threat in faces than individuals without social anxiety.

Effects of distortion of spatial and temporal resolution of video stimuli on emotion attributions

Whether recognition of emotion from facial expression is affected by distortions of pictorial quality has rarely been tested, with the exception of the influence of picture size on emotion recognition. On the other hand, this question is important for (low-cost) tele-communication and tele-conferencing. Here an attempt is made to study whether emotion recognition from facial expression is impaired when video stimuli are distorted both with respect to spatial (pixel) resolution and with respect to temporal resolution (refreshment rate).N=56 stimuli, in which professional actors encoded 14 different emotions, were presented to groups of judges (N=10 in each condition) in six different distortion conditions. Furthermore, judges were confronted with a control condition, presenting the non-distorted stimuli. Channel of information (close-up of the face versus full body recording) was in addition manipulated. Results indicate (besides main effects for type of emotion encoded and for channel) that emotion recognition is impaired by reductions of both spatial resolution and temporal resolution, but that even very low spatial and temporal resolutions result in recognition rates which are still considerably above chance expectation. Results are discussed with respect to the importance of facial expression and body movement in communicating emotions, and with respect to applied aspects concerning tele-communication.