Children's Facial Expressions Research Articles

Facial Expressions of Emotion in Children with Cleft Lip and Palate.

To examine the facial movements children with cleft lip and palate (CLP) employ to express basic emotions. Ability of observers to interpret facial expressions of children with CLP was also considered. Prospective case-control design. Outpatient craniofacial anomalies clinic. Twenty-five children with CLP (age 8 to 12) and 25 age/sex-matched controls. Children were video recorded making facial expressions representing anger, disgust, fear, happiness, sadness, and surprise. Magnitude of children's facial movements was quantified and compared using OpenFace. Subsequently, emotion videos were presented to 19 adults who were asked to identify the emotion conveyed in each facial expression. Accuracy of emotion recognition was compared across groups. Compared with controls, children with CLP employed significantly (P < .05) smaller magnitude superior and lateral perioral movements to express disgust (Cohen's d = .50), happiness (Cohen's d = 1.1), and fear (Cohen's d = .93). For disgust and sadness, children with CLP employed significantly greater magnitude movements of the nose and chin, presumably to compensate for reduced perioral range of motion. For anger, happiness, and sadness, children with CLP employed smaller magnitude movements of the upper face when compared with controls. Observers identified disgust (OR = 1.26), and fear (OR = 2.44) significantly less accurately in children with CLP when compared with controls. Children with CLP employed different facial movements to express certain emotions. Observers less accurately identified some emotions conveyed by facial expressions in children with CLP when compared with controls, likely due in part to differences in facial movements. Future research should explore the implications of these differences for social communication.

On manipulation by emotional AI: UK adults' views and governance implications.

With growing commercial, regulatory and scholarly interest in use of Artificial Intelligence (AI) to profile and interact with human emotion ("emotional AI"), attention is turning to its capacity for manipulating people, relating to factors impacting on a person's decisions and behavior. Given prior social disquiet about AI and profiling technologies, surprisingly little is known on people's views on the benefits and harms of emotional AI technologies, especially their capacity for manipulation. This matters because regulators of AI (such as in the European Union and the UK) wish to stimulate AI innovation, minimize harms and build public trust in these systems, but to do so they should understand the public's expectations. Addressing this, we ascertain UK adults' perspectives on the potential of emotional AI technologies for manipulating people through a two-stage study. Stage One (the qualitative phase) uses design fiction principles to generate adequate understanding and informed discussion in 10 focus groups with diverse participants (n = 46) on how emotional AI technologies may be used in a range of mundane, everyday settings. The focus groups primarily flagged concerns about manipulation in two settings: emotion profiling in social media (involving deepfakes, false information and conspiracy theories), and emotion profiling in child oriented "emotoys" (where the toy responds to the child's facial and verbal expressions). In both these settings, participants express concerns that emotion profiling covertly exploits users' cognitive or affective weaknesses and vulnerabilities; additionally, in the social media setting, participants express concerns that emotion profiling damages people's capacity for rational thought and action. To explore these insights at a larger scale, Stage Two (the quantitative phase), conducts a UK-wide, demographically representative national survey (n = 2,068) on attitudes toward emotional AI. Taking care to avoid leading and dystopian framings of emotional AI, we find that large majorities express concern about the potential for being manipulated through social media and emotoys. In addition to signaling need for civic protections and practical means of ensuring trust in emerging technologies, the research also leads us to provide a policy-friendly subdivision of what is meant by manipulation through emotional AI and related technologies.

Maternal childhood emotional abuse increases cardiovascular responses to children's emotional facial expressions.

Parents with a history of childhood maltreatment may be more likely to respond inadequately to their child's emotional cues, such as crying or screaming, due to previous exposure to prolonged stress. While studies have investigated parents' physiological reactions to their children's vocal expressions of emotions, less attention has been given to their responses when perceiving children's facial expressions of emotions. The present study aimed to determine if viewing facial expressions of emotions in children induces cardiovascular changes in mothers (hypo- or hyper-arousal) and whether these differ as a function of childhood maltreatment. A total of 104 mothers took part in this study. Their experiences of childhood maltreatment were measured using the Childhood Trauma Questionnaire (CTQ). Participants' electrocardiogram signals were recorded during a task in which they viewed a landscape video (baseline) and images of children's faces expressing different intensities of emotion. Heart rate variability (HRV) was extracted from the recordings as an indicator of parasympathetic reactivity. Participants presented two profiles: one group of mothers had a decreased HRV when presented with images of children's facial expressions of emotions, while the other group's HRV increased. However, HRV change was not significantly different between the two groups. The interaction between HRV groups and the severity of maltreatment experienced was marginal. Results suggested that experiences of childhood emotional abuse were more common in mothers whose HRV increased during the task. Therefore, more severe childhood experiences of emotional abuse could be associated with mothers' cardiovascular hyperreactivity. Maladaptive cardiovascular responses could have a ripple effect, influencing how mothers react to their children's facial expressions of emotions. That reaction could affect the quality of their interaction with their child. Providing interventions that help parents regulate their physiological and behavioral responses to stress might be helpful, especially if they have experienced childhood maltreatment.

Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned study.

Although the pediatric perioperative pain management has been improved in recent years, the valid and reliable pain assessment tool in perioperative period of children remains a challenging task. Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults, but also because there is a lack of effective and specific assessment tool for children. In addition, exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae. The short-term sequelae can induce a series of neurological, endocrine, cardiovascular system stress related to psychological trauma, while long-term sequelae may alter brain maturation process, which can lead to impair neurodevelopmental, behavioral, and cognitive function. Children's facial expressions largely reflect the degree of pain, which has led to the developing of a number of pain scoring tools that will help improve the quality of pain management in children if they are continually studied in depth. The artificial intelligence (AI) technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks, which can effectively identify and systematically analyze various subtle features of children's facial expressions. Based on the construction of a large database of images of facial expressions in children with perioperative pain, this study proposes to develop and apply automatic facial pain expression recognition software using AI technology. The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.

Pain detection through facial expressions in children with autism using deep learning

Pain detection through facial expressions in children with autism using deep learning

Voluntary imitation of dynamic facial expressions in attention deficit hyperactivity disorder: a facial-behavior analysis

ABSTRACT Objective The difficulties involved in social interaction among children with attention deficit hyperactivity disorder (ADHD) have been shown in many studies. Based on the knowledge that the imitation of facial expressions is a key factor in social interaction and functionality, the focus of prior studies has been on the evaluation of facial expressions in individuals with ADHD. However, little is known about voluntary facial mimicry in individuals with ADHD. In this context, we aimed to evaluate the voluntary-facial-imitation intensity of dynamic facial expressions in children with ADHD. Method Forty-one children with ADHD and 53 typically developing children were included in the study. Participants were presented with a video of six basic emotions and neutral facial expressions selected from the EU-Emotion Stimulus Set via a screen. After each emotion, the instruction “now imitate it” was given. While the children watched the video, their faces were recorded with a webcam. The intensity of the children’s voluntary facial imitations was examined with a computer vision program (Openface) that performs facial analysis on recorded videos. Results There was no significant difference between the groups in terms of facial emotion recognition accuracy. In group comparisons of voluntary facial mimicry, children with ADHD showed a significantly higher imitation intensity after emotional expressions of sadness, surprise and fear. There was no difference between the groups after the emotions of happiness, anger and disgust. Conclusion This non-obtrusive, noninvasive, and cost-effective method allowed us to measure the quantitative differences in facial mimicry between children with ADHD and typically developing children. Our results contribute new information to the literature by indicating which emotions can be used in the evaluation of social communication skills, as well as intervention targets for these skills, in children with ADHD.

Effects of wearing an opaque or transparent face mask on the perception of facial expressions: A comparative study between Japanese school-aged children and adults.

The negative side effects of mask-wearing on reading facial emotional cues have been investigated in several studies with adults post-2020. However, little is known about children. This study aimed to determine the negative influence of mask-wearing on reading emotions of adult faces by Japanese school-aged children, compared to Japanese adults. We also examined whether this negative influence could be alleviated by using a transparent face mask instead of an opaque one (surgical mask). The performance on reading emotions was measured using emotion categorization and emotion intensity rating tasks for adult faces. As per the findings, the accuracy of emotion recognition in children was impaired for various facial expressions (disgust, fear, happy, neutral, sad, and surprise faces), except for angry faces. Conversely, in adults, it was impaired for a few facial expressions. The perceived intensity for happy faces with a surgical mask was weaker in both children and adults than in those without the mask. A negative influence of wearing surgical masks was generally not observed for faces wearing a transparent mask in both children and adults. Thus, negative side effects of mask-wearing on reading emotions are observed for more facial expressions in children than in adults; transparent masks can help remedy these.

A physiological signal database of children with different special needs for stress recognition

This study presents a new dataset AKTIVES for evaluating the methods for stress detection and game reaction using physiological signals. We collected data from 25 children with obstetric brachial plexus injury, dyslexia, and intellectual disabilities, and typically developed children during game therapy. A wristband was used to record physiological data (blood volume pulse (BVP), electrodermal activity (EDA), and skin temperature (ST)). Furthermore, the facial expressions of children were recorded. Three experts watched the children’s videos, and physiological data is labeled “Stress/No Stress” and “Reaction/No Reaction”, according to the videos. The technical validation supported high-quality signals and showed consistency between the experts.

Can repeated intranasal oxytocin administration affect reduced neural sensitivity towards expressive faces in autism? A randomized controlled trial.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication and interaction. Crucial for efficient social interaction is the ability to quickly and accurately extract information from a person's face. Frequency-tagging electroencephalography (EEG) is a novel tool to quantify face-processing sensitivity in a robust and implicit manner. In terms of intervention approaches, intranasal administration of oxytocin (OT) is increasingly considered as a potential pharmacological approach for improving socio-communicative difficulties in ASD, through enhancing social salience and/or reducing (social) stress and anxiety. In this randomized, double-blind, placebo-controlled, mechanistic pharmaco-neuroimaging clinical trial, we implemented frequency-tagging EEG to conduct an exploratory investigation into the impact of repeated OT administration (4 weeks, 12 IU, twice daily) on neural sensitivity towards happy and fearful facial expressions in children with ASD (8-12 years old; OT: n = 29; placebo: n = 32). Neural effects were assessed at baseline, post-nasal spray (24 hr after the last nasal spray) and at a follow-up session, 4 weeks after the OT administration period. At baseline, neural assessments of children with ASD were compared with those of an age- and gender-matched cohort of neurotypical (NT) children (n = 39). Children with ASD demonstrated reduced neural sensitivity towards expressive faces, as compared to NT children. Upon nasal spray administration, children with ASD displayed a significant increase in neural sensitivity at the post- and follow-up sessions, but only in the placebo group, likely reflecting an implicit learning effect. Strikingly, in the OT group, neural sensitivity remained unaffected from the baseline to the post-session, likely reflecting a dampening of an otherwise typically occurring implicit learning effect. First, we validated the robustness of the frequency-tagging EEG approach to assess reduced neural sensitivity towards expressive faces in children with ASD. Furthermore, in contrast to social salience effects observed after single-dose administrations, repeated OT administration dampened typically occurring learning effects in neural sensitivity. In line with OT's social anxiolytic account, these observations possibly reflect a predominant (social) stress regulatory effect towards emotionally evocative faces after repeated OT administration.

Recognition of Children’s Facial Expressions Using Deep Learned Features

The emotional well-being of a child is crucial for their successful integration into society as a productive individual. While technology has made significant strides in enabling machines to decipher human emotional signals, current research in emotion recognition primarily prioritizes adults, disregarding the fact that children develop emotional awareness at an early stage. This highlights the need to explore how machines can recognize facial expressions in children, although the absence of a standardized database poses a challenge. In this study, we propose a system that employs Convolutional-Neural-Network (CNN)-based models, such as VGG19, VGG16, and Resnet50, as feature extractors, and Support Vector Machine (SVM) and Decision Tree (DT) for classification, to automatically recognize children’s expressions using a video dataset, namely Children’s Spontaneous Facial Expressions (LIRIS-CSE). Our system is evaluated through various experimental setups, including 80–20% split, K-Fold Cross-Validation (K-Fold CV), and leave one out cross-validation (LOOCV), for both image-based and video-based classification. Remarkably, our research achieves a promising classification accuracy of 99% for image-based classification, utilizing features from all three networks with SVM using 80–20% split and K-Fold CV. For video-based classification, we achieve 94% accuracy using features from VGG19 with SVM using LOOCV. These results surpass the performance of the original work, which reported an average image-based classification accuracy of 75% on their LIRIS-CSE dataset. The favorable outcomes obtained from our research can pave the way for the practical application of our proposed emotion recognition methodology in real-world scenarios.

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With the developing technology, smart systems have started to take place in our daily lives. Accordingly, it is very important for the systems that will actively participate in social life to adapt to social life properly. One of the most important steps of adapting to social life is communication. Facial expressions are one of the most important parts of communication that usually supports verbal communication. For this reason, many studies have been carried out on identifying facial expressions. The vast majority of these studies were carried out using datasets containing only adult faces. Conducting studies that do not involve the elderly and children may lead to the creation and development of highly biased smart systems. Therefore, this article focuses on detecting children's facial expressions. In order to detect facial expressions in children, a data set was prepared with images collected from search engines using keywords. By using the transfer learning method, the success of VGG16, ResNet50, DenseNet121, InceptionV3, InceptionResNetV2 and Xception models were evaluated and compared on this prepared data set. According to the evaluation results, the best result was obtained with the InceptionV3 model with an accuracy rate of 76.3% and an F1 score of 0.76.

Study on the Features of Public Space for Children's Emotional Healing Based on Facial Emotion Recognition Technology

This paper uses facial emotion recognition and machine learning techniques to explore the influence of spatial features of children's activity areas on children's emotions. Through the children's evaluation experiment on the emotional quality of public spaces, we obtained the facial expression pictures and the questionnaire data of the emotional self-assessment scale (SAM) when children used the public space. Then use MEGVII facial emotion recognition API to recognize children's facial expressions, and form a facial emotion feature variable dataset. Then, a public space emotional quality model was established using decision tree (DT), neural network (NN), and random forest (RF) classifiers. Finally, the model's performance was evaluated through the confusion matrix, and five groups of data reserved in the original dataset were used for external validation of the model. The results show that: 1) In the binary classification model, the classification accuracy of the emotional valence of the NN classifier was 94.44%, and the classification accuracy of arousal was 84.62%. These two results outperform the models built by the DT and RF classifiers. In the three-class model, the classification accuracy of the emotional valence of the DF classifier is 71.88%, and the accuracy of arousal is 84.62%. These two results outperformed the models built by NN and RF classifiers. 2) The analysis results of the correlation between spatial features and emotional quality showed that the width of the bordering tree, width-to-height-ratio of the bordering tree (w2/h2), the number of layers of the border, the continuity of the spatial border, the proportion of facilities, the color of the space, the number of types and pavement color types were usually correlated with the spatial emotional valence.

The age bias in labeling facial expressions in children: Effects of intensity and expression.

Emotion reasoning, including labeling of facial expressions, is an important building block for a child's social development. This study investigated age biases in labeling facial expressions in children and adults, focusing on the influence of intensity and expression on age bias. Children (5 to 14 years old; N = 152) and adults (19 to 25 years old; N = 30) labeled happiness, disgust or sadness at five intensity levels (0%; 25%; 50%; 75%; and 100%) in facial images of children and adults. Sensitivity was computed for each of the expression-intensity combinations, separately for the child and adult faces. Results show that children and adults have an age bias at low levels of intensity (25%). In the case of sadness, children have an age bias for all intensities. Thus, the impact of the age of the face seems largest for expressions which might be most difficult to recognise. Moreover, both adults and children label most expressions best in adult rather than child faces, leading to an other-age bias in children and an own-age bias in adults. Overall, these findings reveal that both children and adults exhibit an age bias in labeling subtle facial expressions of emotions.

Mothers need more information to recognise associated emotions in child facial expressions

ABSTRACT Parenting requires mothers to read social cues and understand their children. It is particularly important that they recognise their child’s emotions to react appropriately, for example, with compassion to sadness or compersion to happiness. Despite this importance, it is unclear how motherhood affects women’s ability to recognise emotions associated with facial expressions in children. Using videos of an emotionally neutral face continually and gradually taking on a facial expression associated with an emotion, we quantified the amount of information needed to match the emotion with the facial expression. Mothers needed more information than non-mothers to match the emotions with the facial expressions. Both mothers and non-mothers performed equally on a control task identifying animals instead of emotions, and both groups needed less information when recognising the emotions associated with facial expressions in adolescents than pre-schoolers. These results indicate that mothers need more information for to correctly recognise typically associated emotions in child facial expressions but not for similar tasks not involving emotions. A possible explanation is that child facial expressions associated with emotions may have a greater emotional impact on mothers than non-mothers leading to task interference but possibly also to increased compassion and compersion.

Face Expression Classification in Children Using CNN

One of the turbulent emotions can be recognized from facial expressions. When compared with adults, children's facial expressions are more expressive for positive emotions and ambiguous for negative emotions so that they are much more difficult to recognize. Ambiguous in terms of negative emotions, for example, when children are angry, sometimes they show an expressionless face, making it difficult to know what emotions the child is experiencing. Therefore, it is proposed research using Convolutional Neural Network with ResNet-50 architecture. According to [1] CNN Resnet-50 is superior to other facial recognition methods, specifically in the classification of facial expressions. CNN ResNet-50 generates a model during the training process, and the model will be used during the testing process. The dataset used is Children's Spontaneous facial Expressions (LIRIS-CSE) data proposed by [2]. CNN ResNet-50 can identify children's expressions well, including expressions of anger, disgust, fear, happy, sad and surprise. The results showed a very significant increase in accuracy, namely in testing data testing reached 99.89%.

Emotional face expressions recognition in childhood: developmental markers, age and sex effect.

Recognizing emotional face expressions in others is a valuable non-verbal communication and particularly relevant throughout childhood given that children's language skills are not yet fully developed, but the first interactions with peers have just started. This study aims to investigate developmental markers of emotional facial expression in children and the effect of age and sex on it. A total of 90 children split into three age groups: 6-7years old (n = 30); 8-9years old (n = 30); 10-11years old (n = 30) took part in the study. Participants were exposed to 38 photos in two exposure times (500ms and 1000ms) of children expressing happiness, sadness, anger, disgust, fear and surprise on three intensities, plus images of neutral faces. Happiness was the easiest expression to be recognized, followed by disgust and surprise. As expected, 10-11-year-old group showed the highest accuracy means, whereas 6-7-year-old group had the lowest means of accuracy. Data support the non-existence of female advantage.

Racialized emotion recognition accuracy and anger bias of children's faces.

Research suggests that individuals are racially biased when judging the emotions of others (Elfenbein & Ambady, 2002) and particularly regarding attributions about the emotion of anger (Halberstadt, Castro, Chu, Lozada, & Sims, 2018; Hugenberg & Bodenhausen, 2003). Systematic, balanced designs are rare, and are comprised of adults viewing adults. The present study expands the questions of racialized emotion recognition accuracy and anger bias to the world of children. Findings that adults demonstrate either less emotion accuracy and/or greater anger bias for Black versus White children could potentially explain some of the large racialized disciplinary discrepancies in schools. To test whether racialized emotion recognition accuracy and anger bias toward children exists, we asked 178 prospective teachers to complete an emotion recognition task comprised of 72 children's facial expressions depicting six emotions and divided equally by race (Black, White) and gender (female, male). We also assessed implicit bias via the child race Implicit Association Test and explicit bias via questionnaire. Multilevel modeling revealed nuanced racialized emotion recognition accuracy with a race by gender interaction, but clear racialized anger bias toward both Black boys and girls. Both Black boys and Black girls were falsely seen as angry more often than White boys and White girls. Higher levels of either implicit or explicit bias did not increase odds of Black children being victim to anger bias, but instead decreased odds that White children would be misperceived as angry. Implications for addressing preexisting biases in teacher preparation programs and by children and parents are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Eye Tracking Research on the Influence of Spatial Frequency and Inversion Effect on Facial Expression Processing in Children with Autism Spectrum Disorder.

Facial expression processing mainly depends on whether the facial features related to expressions can be fully acquired, and whether the appropriate processing strategies can be adopted according to different conditions. Children with autism spectrum disorder (ASD) have difficulty accurately recognizing facial expressions and responding appropriately, which is regarded as an important cause of their social disorders. This study used eye tracking technology to explore the internal processing mechanism of facial expressions in children with ASD under the influence of spatial frequency and inversion effects for improving their social disorders. The facial expression recognition rate and eye tracking characteristics of children with ASD and typical developing (TD) children on the facial area of interest were recorded and analyzed. The multi-factor mixed experiment results showed that the facial expression recognition rate of children with ASD under various conditions was significantly lower than that of TD children. TD children had more visual attention to the eyes area. However, children with ASD preferred the features of the mouth area, and lacked visual attention and processing of the eyes area. When the face was inverted, TD children had the inversion effect under all three spatial frequency conditions, which was manifested as a significant decrease in expression recognition rate. However, children with ASD only had the inversion effect under the LSF condition, indicating that they mainly used a featural processing method and had the capacity of configural processing under the LSF condition. The eye tracking results showed that when the face was inverted or facial feature information was weakened, both children with ASD and TD children would adjust their facial expression processing strategies accordingly, to increase the visual attention and information processing of their preferred areas. The fixation counts and fixation duration of TD children on the eyes area increased significantly, while the fixation duration of children with ASD on the mouth area increased significantly. The results of this study provided theoretical and practical support for facial expression intervention in children with ASD.

Распознавание эмоциональных состояний детей с синдромом Дауна по мимической экспрессии: перцептивный и автоматический анализ динамических изображений

The study is devoted to the investigation of the recognition of the emotional state of children with Down syndrome (DS) by their facial expression. For this purpose, a series of perceptual experiments involving adults (n=75) and automatic analysis of the facial expressions of children (n=35, aged 5—16 years) were carried out using the FaceReader program. The ability of adults to recognize the emotional states of children: joy — neutral (calm state) — sadness — anger, by open faces and faces with masks over the eyes and mouth is shown. Better recognition of the state of joy and neutral state under the condition of an open face and a decrease in recognition accuracy in a mask in the eye area compared to the absence of a mask and a mask in the mouth area were found. Automatic recognition of the states of joy and neutral states is better than the states of sadness and anger, if the face is open and the mask in the mouth area of the child. The conditions for use the automatic recognition of facial expression in children with DS and for applying the method of perceptual analysis for identifying the specificity of the child emotional sphere development are discussed.

Children's Facial Expressions During Collaborative Coding: Objective Versus Subjective Performances

Children's Facial Expressions During Collaborative Coding: Objective Versus Subjective Performances