People's Facial Expressions Research Articles

Elevating sentiment analysis with VGG-16's facial expression insights

<span lang="EN-US">In today's data-driven world, the ability to analyze emotional responses is essential. The pressing necessity that drives this study is to revolutionize the field of sentiment analysis by extracting the hidden information from people's facial expressions. It examines people's preferences, worries, and pleasure, revealing their views on many topics. Beyond text-based sentiment analysis, this research adds facial expression-based sentiment analysis into existing systems for tailored recommendations and mental health monitoring. The system emphasizes visual stimuli's emotional influence to improve decision-making, content adaptability, and user experiences. The implementation involves transfer learning with the pre-trained VGG-16 model, which enhances ability to discern intricate emotional cues from facial expressions. Convolutional Neural Network (CNN) and contextual analysis allow the model to understand users' emotions and provide insights into their thoughts, feelings, and behaviours. To improve emotion recognition reliability and reactivity, this study examines Random Forest, Support Vector Machine (SVM), and CNN methodologies. The VGG-16 CNN model outperforms over SVM and Random Forest classifiers with accuracy of 95%. This study highlights facial expression-based sentiment analysis.</span>

Exploration of Emotion Analysis and Personalized Emotion Adjustment Algorithm in English Learning Process

An important component of human existence and behaviour is emotion. The automatic identification of emotions has gained significant importance in the domains of affective computing and human-machine interaction in recent times. Getting pictures of people's facial expressions is one of the simplest and least expensive methods for identifying emotions among the several physiological and kinematic data that may be utilised. Because of variations in anatomy, culture, and environment, developing a generalised, cross-subject model for emotion identification from facial expression remains difficult.This study presents an intelligent classroom that uses e-learning to help persons with autism spectrum condition strengthen their emotional abilities. Monitoring autism child engagement and participation in class while looking for signs of attention is necessary for effective classroom education. Teaching quality is increasingly being determined by the teachers' capacity to assess and evaluate the behaviour of their pupils in the classroom. Modern classroom systems are enhanced with the most recent technologies in educational institutions to make them more interactive, student-centered, and personalised. Even with these tools, it can be challenging for teachers to gauge their pupils' levels of interest and focus. This study makes use of contemporary technology to establish a real-time, intelligent vision-based classroom that can track students' moods, attendance, and levels of focus even when they are wearing face masks. In order to evaluate students' attention or lack thereof in a classroom, we trained AlexNet models that recognise student behaviour, including recognising facial expressions. Another designed to manage the irregular relationship between pupils and activities is the Interaction Students Education Neural Network ( Int_Edu_NN). The Q-matrix is used to firstly determine the pupil variable and the assignment indicator. This network is analyzed using two datasets in terms of various parameters and hence the proposed Int_Edu_NN achieves 99.% and 98% of accuracy.

Museum Achieving Future Sustainable Development: A Study on the Attraction of Historical Exhibits in Museum Nursing Direction

The aim of this study is to explore a method that combines Digital Twins (DTs) with Convolutional Neural Network (CNN) algorithms to analyze the attractiveness of historical and cultural exhibits in museums and humanistic care, in order to achieve intelligent and digital development of exhibitions under museum humanistic care. The concept of "Health Museum and Health Management" has received initial attention and rapid response from the nursing community in Europe and America. Its essence emphasizes the intervention of museum intelligence in medical and health care and the role of improving the medical and health system, creating the medical service function of museums. Firstly, using DTs technology to digitally model the historical and cultural exhibits of the museum, achieving the display and interaction of virtual exhibits. Then, the Mini_Xception network was proposed to improve the CNN algorithm and combined with the ResNet algorithm to construct a facial emotion recognition model. Finally, using this model, the attractiveness of museum historical and cultural DTs exhibits was accurately predicted by recognizing people's facial expressions. The comparative experimental results show that this recognition method can greatly improve recognition accuracy and scalability. Compared with traditional recognition methods, the recognition accuracy can be improved by 5.53%, and 2.71s can reduce the data transmission delay of the model. The scalability of enhanced recognition types can also meet real-time interaction requirements in a shorter amount of time. This study has important reference value for the digital and intelligent development of museums combined with nursing exhibitions.

Exploring the Function of Expressions in Negotiation: The DyNego-WOZ Corpus

For affective computing to have an impact outside the laboratory, facial expressions must be studied in rich naturalistic situations. We argue negotiations are one such situation as they are ubiquitous in daily life, often evoke strong emotions, and perceived emotion shapes decisions and outcomes. Negotiations are a growing focus in AI research and applications, including agents that negotiate directly with people and attempt to use affective information. We introduce the DyNego-WOZ Corpus, which includes dyadic negotiation between participants and wizard-controlled virtual humans. We demonstrate the value of this corpus to the affective computing community by examining participants' facial expressions in response to a virtual human negotiation partner. We show that people's facial expressions typically co-occur with the end of their partner's speech (suggesting they reflect a reaction to the content of this speech), that these reactions do not correspond to prototypical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emotional</i> expressions, and that these reactions can help predict the expresser's subsequent action. We highlight challenges in working with such naturalistic data, including difficulties of expression recognition during speech, and the extreme variability of expressions, both across participants and within a negotiation. Our findings reinforce arguments that facial expressions convey more than emotional state but serve important communicative functions.

Incongruent gestures slow the processing of facial expressions in university students with social anxiety.

In recent years, an increasing number of studies have examined the mechanisms underlying nonverbal emotional information processing in people with high social anxiety (HSA). However, most of these studies have focused on the processing of facial expressions, and there has been scarce research on gesture or even face-gesture combined processing in HSA individuals. The present study explored the processing characteristics and mechanism of the interaction between gestures and facial expressions in people with HSA and low social anxiety (LSA). The present study recruited university students as participants and used the Liebowitz Social Anxiety Scale scores to distinguish the HSA and LSA groups. We used a 2 (group: HSA and LSA) × 2 (emotion valence: positive, negative) × 2 (task: face, gesture) multifactor mixed design, and videos of a single face or gesture and combined face-gesture cues were used as stimuli. We found that (1) there is a distinction in the processing of faces and gestures, with individuals recognizing gestures faster than faces; (2) there is an attentional enhancement in the processing of gestures, particularly for negative gestures; and (3) when the emotional valence of faces and gestures align, it facilitates the recognition of both. However, incongruent gestures have a stronger impact on the processing of facial expressions compared to facial expressions themselves, suggesting that the processing of facial emotions is more influenced by environmental cues provided by gestures. These findings indicated that gestures played an important role in emotional processing, and facial emotional processing was more dependent on the environmental cues derived from gestures, which helps to clarify the reasons for biases in the interpretation of emotional information in people with HSA.

Human emotion recognition with convolutional neural network

The interaction between intelligent robots and humans has always been a hot issue, and researchers hope to make human-robot interaction as harmonious as human-human interaction. To achieve this, it is particularly important to enable robots to recognize human facial emotions automatically. However, many intelligent robots can already understand people's emotions through vocal communication. However, some people do not like to express their feelings through words, so it would be more convenient to let machines can automatically analyze people's facial emotions. This paper aims to make the machine recognize people's facial expressions and automatically analyze their emotions to make human-computer interaction more harmonious. The convolutional neural network has shown great influence on image feature extraction in the development of the machine learning field today. Therefore, this paper will adopt the advanced method of CNN to train the model on the FER2013 dataset. The abundant experiments demonstrate that the final trained model has good accuracy in recognizing three emotions: happy, surprise, and neutral.

Effects of other people's facial emotional expression on consumers' perceptions of chocolate chip cookies containing cricket protein.

Edible insects are recognized as a potential alternative and sustainable source of high-quality protein for the human diet. Entomophagy is highly related to negative emotions that may cause reluctance to adopt insects as food in Western countries. During human interaction, a person's facial emotional expression (FEE) may influence other people's emotional responses. A person's emotional state may affect his/her food preference and food choice. Understanding how other people's FEE would affect consumers' emotional profiles, liking, and subsequent willingness to try (WTT) and purchase intent (PI) toward insect-containing food products may help increase the acceptance of entomophagy. This study identified emotional responses toward chocolate chip cookies containing cricket protein using valence and arousal scales in order to explore the effects of other people's FEE (positive, negative, and/or sensation seeking) and to find the correlation between consumers' emotional and overall liking (OL) responses for cricket-containing chocolate chip cookies. Predicting PI for such cookies was also performed. For consumers who perceived positive emotion from other people's FEE after watching a short video clip, their emotional feeling was raised on both valence and arousal dimensions, while negative FEE stimulus imparted the opposite effects. The OL scores and emotional intensities after watching the three FEE videos were highly related to consumers' PI. Males compared to females rated the cricket-containing cookies higher on positive emotion intensity, OL, and PI. Among the three FEEs evaluated, the positive emotional stimulus would be beneficial in increasing acceptance, WTT, and PI of insect-containing foods. PRACTICAL APPLICATION: Edible insects are potentially alternative and sustainable sources of high-quality protein for the human diet. Entomophagy is highly related to negative emotions that cause reluctance to adopt insects as food in Western countries. Other people's facial emotional expressions (FEEs) may affect consumer food-evoked emotional profiles, overall liking (OL), and purchase intent (PI). For consumers who perceived positive emotion from other people's FEE, their emotional feeling was raised on both valence and arousal dimensions, and OL scores and emotion intensities were highly related to consumers' PI. Exploiting positive emotional stimuli as demonstrated in this study would be beneficial in increasing acceptance of insect-containing food.

Fusion Fuzzy Logic and Deep Learning for Depression Detection Using Facial Expressions

One of the critical issues in detecting depression is using facial expressions with image data classification. In This research paper, we proposed Fusion Fuzzy Logic((FFL) with deep learning for identifying depressed people based on their facial expressions. Our proposed model the based on an advanced fuzzy algorithm with deep learning for unordered fuzzy rule(FR) initiation to offer appropriate and suitable opinions based on depressed people's facial expressions(FE), to allow Depression Recognition(DR) from image files and recorded video files. The primary goal of this research work was to use the fusion method to turn these facial expressions (FE) into the detection of depressed states. To elevate the performance of the Fusion Fuzzy Logic((FFL) (fuzzy logic and CNN)), delivering them entreated them several times to imitate specific facial expressions. Our proposed FFL with the CNN model produces exact and dependable results with a 94.3% overall accuracy comparable to human recognition.

Reading emotional faces in deaf and hard-of-hearing and typically hearing children.

Reading emotions from other people's facial expressions is an important skill that guides social interactions. With limited auditory input and atypical emotion socialization, deaf and hard-of-hearing (DHH) children may develop atypical processing patterns when reading emotional faces. The current study aimed at understanding whether and how DHH and typically hearing (TH) children differed at 3 emotion processing levels: gaze patterns, physiological arousal, and interpretation. Fifty-five DHH children and 72 TH children completed an emotional face matching task in which they were presented with happy, angry, fearful, and emotionally neutral faces. During the task participants' eye gazes and pupil diameter were measured by an eye-tracking device. The DHH and TH children both paid most attention to the eye region when reading emotional faces. Yet, a contrast between happy faces and nonhappy faces was observed in physiological arousal and interpretation tendency in the DHH children only: Nonhappy facial expressions were more arousing and were confused more often than happy expressions, which may reflect the DHH children being less experienced in processing nonhappy expressions due to limited access to the social environment. The results highlighted the importance of looking into the qualitative differences between typical and atypical development. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Individual differences in spontaneous facial expressions in people with visual impairment and blindness

People can visualize their spontaneous and voluntary emotions via facial expressions, which play a critical role in social interactions. However, less is known about mechanisms of spontaneous emotion expressions, especially in adults with visual impairment and blindness. Nineteen adults with visual impairment and blindness participated in interviews where the spontaneous facial expressions were observed and analyzed via the Facial Action Coding System (FACS). We found a set of Action Units, primarily engaged in expressing the spontaneous emotions, which were likely to be affected by participants’ different characteristics. The results of this study could serve as evidence to suggest that adults with visual impairment and blindness show individual differences in spontaneous facial expressions of emotions.

Facial expression stereotypes of rich and poor adults and children.

Facial expression stereotypes can affect the perception of other people's facial expressions. This study examined facial expression stereotypes of poor and rich adults and children. Experiment 1 found that the adult participants associated rich adults with positive emotions (i.e., happiness) and poor adults with negative emotions (i.e., sadness). In Experiments 2-4, adult participants still thought that rich 4-, 6-, and 10-year-old children would show positive facial expressions (happiness) but did not think that poor 4-, 6-, and 10-year-old children would show negative emotions (sadness). These finding have implications concerning how adults communicate with poor and rich adults and children.

Recognition of emotions through facial expression analysis

The possibility of recognizing what emotion one of our peers is experiencing has been the subject of study by various researchers over the years, Paul Ekman being the one who has delved most deeply into this subject, the most viable and simple way to achieve this would be through the analysis of people's facial expressions. The search for information was carried out using rigorous exclusion criteria such as studies corresponding to grizzly data and letters to the editor, and inclusion criteria such as studies published only in high impact journals such as PubMed, Elsevier, Taylor &amp; Francis, ScienceDirect, APA PsycNet and Springer, PRISMA guidelines and AMSTAR check-list were used. The main objective of this systematic review was to determine whether there is sufficient scientific literature evidence to clarify whether it is possible to accurately identify the six basic universal emotions "happiness, surprise, sadness, anger, fear and disgust" proposed by Paul Ekman through facial expressions. After the analysis of the articles collected and based on the main findings, it is concluded that the recognition of emotions through facial expressions is a subject that still needs to be studied in greater depth, as suggested by the results obtained.

The application of motion capture technology in a clinical evaluation and a therapy for people with autism spectrum disorder

IntroductionAutism spectrum disorder (ASD) encopasses disorders with incompletely known etiology. Facial expression of people with ASD does not often reflect their emotions adequately or are strongly limited. In addition, they have a problem with joint attention. The symptoms of autism spectrum disorder are very various and have different severity that can change over time.There are still no objective methods for estimating these symptoms, which creates a huge diagnostic and clinical problem. Motion Capture technology makes the possibility of this objective assessment of the severity of initial symptoms, their change over time, as well as specificity for people with ASD.ObjectivesTo assess the application of Motion Capture technology in a clinical evaluation and a therapy for people with ASD.MethodsWe analyzed literature related to the topic available at medical bases: PubMed, ResearchGate and Google Scholar. The articles which were included had been published after 2000 and have an English or Polish abstract.ResultsWe included 2 trials involving 81 participants (children and adolescents): 1 trial reported on quantifying the social symptoms of autism and 1 trial on differences of facial expressions in people with and without ASD.ConclusionsThis capture of motions and the analysis of specific movements of people with autism spectrum disorder might be very useful in clinical practice, scientific research, therapy and also in creation of functioning systems at homes, schools and kindergartens. Thanks to this, people with ASD will be able to function better in society.DisclosureNo significant relationships.

Analisis Semiotika Ekspresi Wajah di dalam Ayat-ayat Agung al-Qur’an

Semiotics is the study of signs and symbols as elements and analysis of communication systems. Many verses of the Qur'an show aspects of communication between humans and verses of the Qur'an. This article discusses the study of semiotics with a descriptive analysis approach. The purpose of this essay is to analyze facial expressions in the verses of the Great Qur'an that are expressed verbally. This essay uses Peirce's theory of semiotics in analyzing the facial expressions of people in the world and the faces of people in the afterlife.

Emotion Detection of Human Face

Facial emotion analysis is the basic idea to train the system to understand the different facial expressions of human beings. The Facial expressions are recorded by the use of camera which is attached to user device. Additionally this project will be helpful for the online marketing of the products as it can detect the facial expressions and sentiment of the person. It is the study of people sentiment, opinions and emotions. Sentiment analysis is the method by which information is taken from the facial expressions of people in regard to different situations. The main aim is to read the facial expressions of the human beings using a good resolution camera so that the machine can identify the human sentiments. Convolutional neural network is used as an existing system which is unsupervised neural network to replace that with a supervised mechanism which is called supervised neural network. It can be used in gaming sector, unlock smart phones, automated facial language translation etc.

Facial Expression Production in Autism: A Meta‐Analysis

This meta-analysis summarized studies that examined group differences on the production of facial expressions in participants with ASD compared to typically developing or nonautistic clinical comparison groups. The overall summary effect from 67 effect sizes representing the average ASD-comparison group differences in facial expressions was -0.481, indicating a moderate effect size. We conducted subgroup analyses to group effect sizes according to separate facial expression abilities identified in the literature. These analyses revealed that participants with ASD display facial expressions less frequently and for less amount of time, and they are less likely to share facial expressions with others or automatically mimic the expressions of real faces or face stimuli. Their facial expressions are also judged to be lower in quality and are expressed less accurately. However, participants with ASD do not express emotions less intensely, nor is their reaction time of expression onset slower in response to odors, startling sensations, or in response to face stimuli in mimicry studies. ASD-comparison group differences were moderated by matching procedures, age, and intellectual functioning of the ASD participants suggesting that persons with higher IQ and larger number of accumulated life experiences are better able to produce facial expressions that are more consistent with "neurotypical" norms. Group differences were also stronger for "covertly elicited" than "explicitly elicited" facial expressions suggesting individuals with ASD may naturally produce facial expressions differently from other populations, but are less impaired in expressing emotions typically when prompted to do so in a laboratory setting. Autism Research 2018, 11: 1586-1601. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We reviewed studies that compared facial expressions in people with and without autism. Results revealed that facial expressions of people with autism are atypical in appearance and quality and are used atypically to regulate social interactions. The magnitude of these differences was influenced by participant characteristics (e.g. age and intellectual functioning), and by how facial expressions were measured and analyzed in various studies.

Seeing Fear: It’s All in the Eyes?

Is an amygdala necessary to experience and perceive fear? Intriguing evidence comes from patient S.M. who lost her left and right amygdalae to disease. Initial testing suggested that S.M.'s most defining symptom was an inability to recognize fear in other people's facial expressions. A fascinating paper by Adolphs and colleagues in 2005 examined one potential mechanism for this impairment: a failure to spontaneously attend to widened eyes, the most distinctive physical feature portrayed in symbolic fear expressions. This study helped to invigorate debates about the brain basis of fear and paved the way for a more nuanced understanding of amygdalar function.

The Effectiveness of Technology-Based Intervention in Improving Emotion Recognition Through Facial Expression in People with Autism Spectrum Disorder: a Systematic Review

The use of technology in intervention is not uncommon for improving emotion recognition of individuals with autism spectrum disorders (ASD). The purpose of this paper is to examine the effectiveness of technology-based interventions in emotion recognition in people with ASD. After searching six electronic databases, 15 studies published from 2000 to 2016 were yielded and reviewed. Eight technology-based interventions for improving emotion recognition skills in people with ASD were identified. Overall, this review provides preliminary evidence that the use of technology-based intervention with coach support and emphasis on facial features can improve emotion recognition in children and adolescents with ASD. Factors which need to be considered in order to confirm the effectiveness of various intervention programs were discussed.

Representational momentum in dynamic facial expressions is modulated by the level of expressed pain: Amplitude and direction effects.

Humans have developed a specific capacity to rapidly perceive and anticipate other people's facial expressions so as to get an immediate impression of their emotional state of mind. We carried out two experiments to examine the perceptual and memory dynamics of facial expressions of pain. In the first experiment, we investigated how people estimate other people's levels of pain based on the perception of various dynamic facial expressions; these differ both in terms of the amount and intensity of activated action units. A second experiment used a representational momentum (RM) paradigm to study the emotional anticipation (memory bias) elicited by the same facial expressions of pain studied in Experiment 1. Our results highlighted the relationship between the level of perceived pain (in Experiment 1) and the direction and magnitude of memory bias (in Experiment 2): When perceived pain increases, the memory bias tends to be reduced (if positive) and ultimately becomes negative. Dynamic facial expressions of pain may reenact an "immediate perceptual history" in the perceiver before leading to an emotional anticipation of the agent's upcoming state. Thus, a subtle facial expression of pain (i.e., a low contraction around the eyes) that leads to a significant positive anticipation can be considered an adaptive process-one through which we can swiftly and involuntarily detect other people's pain.

Mapping spontaneous facial expression in people with Parkinson's disease: A multiple case study design.

People with Parkinson’s disease (PD) often experience a decrease in their facial expressivity, but little is known about how the coordinated movements across regions of the face are impaired in PD. The face has neurologically independent regions that coordinate to articulate distinct social meanings that others perceive as gestalt expressions, and so understanding how different regions of the face are affected is important. Using the Facial Action Coding System, this study comprehensively measured spontaneous facial expression across 600 frames for a multiple case study of people with PD who were rated as having varying degrees of facial expression deficits, and created correlation matrices for frequency and intensity of produced muscle activations across different areas of the face. Data visualization techniques were used to create temporal and correlational mappings of muscle action in the face at different degrees of facial expressivity. Results showed that as severity of facial expression deficit increased, there was a decrease in number, duration, intensity, and coactivation of facial muscle action. This understanding of how regions of the parkinsonian face move independently and in conjunction with other regions will provide a new focus for future research aiming to model how facial expression in PD relates to disease progression, stigma, and quality of life.