Development Of Facial Expression Recognition Research Articles

Synchrony or asynchrony: development of facial expression recognition from childhood to adolescence based on large-scale evidence.

The development of facial expression recognition ability in children is crucial for their emotional cognition and social interactions. In this study, 510 children aged between 6 and 15 participated in a two forced-choice task of facial expression recognition. The findings supported that recognition of the six basic facial expressions reached a relatively stable mature level around 8-9 years old. Additionally, model fitting results indicated that children showed the most significant improvement in recognizing expressions of disgust, closely followed by fear. Conversely, recognition of expressions of happiness and sadness showed slower improvement across different age groups. Regarding gender differences, girls exhibited a more pronounced advantage. Further model fitting revealed that boys showed more pronounced improvements in recognizing expressions of disgust, fear, and anger, while girls showed more pronounced improvements in recognizing expressions of surprise, sadness, and happiness. These clear findings suggested the synchronous developmental trajectory of facial expression recognition from childhood to adolescence, likely influenced by socialization processes and interactions related to brain maturation.

ERR-Net: Facial Expression Removal and Recognition Network With Residual Image

Facial expression recognition is an important part of computer vision and has attracted great attention. Although deep learning pushes forward the development of facial expression recognition, it still faces huge challenges due to unrelated factors such as identity, gender, and race. Inspired by decomposing an expression into two parts: neutral component and expression component, we define residual features and propose an end-to-end network framework named Expression Removal and Recognition Network (ERR-Net), which can simultaneously perform expression removal and recognition tasks. The residual features are represented in two ways: pixel level and facial landmark level. Our network focuses on interpreting the encoder’s output and corresponding its segments to expressions to maximize the inter-class distances. We explore the improved generative adversarial network to convert different expressions into neutral expressions (i.e., expression removal), take the residual images as the output, learn the expression components in the process, and realize the classification of expressions. Through sufficient ablation experiments, we have proved that various improvements added on the network have obvious effects. Experimental comparisons on two benchmarks CK+ and MMI demonstrate that our proposed ERR-Net surpasses the state-of-the-art methods in terms of accuracy.

Facial Expression Recognition Model Based on CNN and Data Augmentation Method

Face expressions are essential in expressing human emotions, and it is accomplished by separating features and categorizing them. Facial expression recognition technology has been widely employed in human-computer interaction, telemedicine, mental health, and criminal investigation detection. In recent years, significant advances in deep learning have facilitated the development of facial expression recognition, making it increasingly accessible. Convolutional neural networks (CNN) are the foundation of the face expression recognition model presented in this article. In order to maximize the final accuracy, an abundance of sample photographs are required for training and optimization. As a result, the 35,886 facial expression photos from the FER2013 dataset, which contains all seven emotions, were used for both training and testing. The photos were scaled down to 4848 pixels during data preparation, and data augmentation was carried out. To get accurate face expression recognition results, various optimizers and parameters were chosen for the training network, which was a bespoke structure based on the VGG network design. The model constructed in this study achieved an accuracy of 73.16% during prediction.

Uncertain and biased facial expression recognition based on depthwise separable convolutional neural network with embedded attention mechanism

Datasets play a crucial role in the development of facial expression recognition (FER), but most of the datasets suffer from obvious uncertainties and biases caused by different cultures and collection conditions. To look deeper into these issues, this paper first conducts two sets of experiments, face detection, and facial expression (FE) classification. They are based on three datasets (CK+, FER2013, and RAF–DB), which are collected from lab and wild environments. This paper proposes a network, depthwise separable convolutional neural network (CNN) with an embedded attention mechanism (DSA–CNN) for expression recognition. First, at the preprocessing stage, we obtain the maximum expression range clipping, which is calculated from 81 facial landmark points to filter nonface interferences. Then, we use DSA–CNN, which is based on a coordinate squeeze-and-excitation (CSE) attention for feature extraction. Finally, to further deal with imbalanced class biases and uncertainties issues, this paper proposes a class-weighted cross-entropy loss (CCE-loss) to alleviate the imbalance among seven emotional classes. Then, we combine CCE-loss with ranking regularization loss (RR-loss) and self-importance weighting cross-entropy loss (SCE-loss) at label amend stage, to jointly guide the training of the network. Extensive experiments on three FER datasets demonstrate that our proposed method outperforms most of the state-of-the-art methods eventually.

Development of facial expression recognition following extended blindness: The importance of motion

Development of facial expression recognition following extended blindness: The importance of motion

Facial Expression Recognition in Children with Cochlear Implants and Hearing Aids.

Facial expression recognition (FER) is an important aspect of effective interpersonal communication. In order to explore whether the development of FER was delayed in hearing impaired children, 44 child participants completed labeling, and matching tasks to identify four basic emotions (happiness, sadness, anger, and fear). Twenty-two participants had either a cochlear implant (CI) or a hearing aid (HA) while 22 had normal hearing and participants were matched across conditions by age and gender. The results showed that children with a CI or HA were developmentally delayed not only in their emotion-labeling (verbal) tasks but also in their emotion-matching (nonverbal) tasks. For all participants, the emotion-labeling task was more difficult than the emotion-matching task. Additionally, the relative difficulty of recognizing four different emotional expressions was similar between verbal and nonverbal tasks.

Mapping the development of facial expression recognition.

Reading the non-verbal cues from faces to infer the emotional states of others is central to our daily social interactions from very early in life. Despite the relatively well-documented ontogeny of facial expression recognition in infancy, our understanding of the development of this critical social skill throughout childhood into adulthood remains limited. To this end, using a psychophysical approach we implemented the QUEST threshold-seeking algorithm to parametrically manipulate the quantity of signals available in faces normalized for contrast and luminance displaying the six emotional expressions, plus neutral. We thus determined observers' perceptual thresholds for effective discrimination of each emotional expression from 5 years of age up to adulthood. Consistent with previous studies, happiness was most easily recognized with minimum signals (35% on average), whereas fear required the maximum signals (97% on average) across groups. Overall, recognition improved with age for all expressions except happiness and fear, for which all age groups including the youngest remained within the adult range. Uniquely, our findings characterize the recognition trajectories of the six basic emotions into three distinct groupings: expressions that show a steep improvement with age - disgust, neutral, and anger; expressions that show a more gradual improvement with age - sadness, surprise; and those that remain stable from early childhood - happiness and fear, indicating that the coding for these expressions is already mature by 5 years of age. Altogether, our data provide for the first time a fine-grained mapping of the development of facial expression recognition. This approach significantly increases our understanding of the decoding of emotions across development and offers a novel tool to measure impairments for specific facial expressions in developmental clinical populations.

Facial mimicry interference affects reaction time but not accuracy on an emotion recognition task in children, adolescents and adults

Adults display superior performance on tasks of facial expression recognition when compared to children and adolescents. Embodied simulation strategies contribute to emotion recognition performance in adults. To date however, the use of such strategies in children and adolescents has not been examined. The present study investigated the development of facial expression recognition from an embodied perspective in a sample of 43 children (Mean age = 8.02 years), 35 adolescents (Mean age = 12.69 years) and 39 adults (Mean age = 19.92 years). In a three-alternative forced-choice emotion recognition task, children and adolescents were less accurate at recognising faces displaying sadness than they were at recognising happiness. Adults were equally as accurate at identifying happiness and sadness. All groups were less accurate at recognising fear than recognising both happiness and sadness. In trials in which the emotions were accurately recognised, all age groups were significantly slower at recognising fear t...

The specific impairment of fearful expression recognition and its atypical development in pervasive developmental disorder

Several studies have examined facial expression recognition in pervasive developmental disorder (PDD), including autism and Asperger's disorder, but the results have been inconsistent. We investigated the relationship between facial expression recognition and age, face recognition, and symptom severity. Subjects were 28 individuals with mild PDD subtypes and 28 age- and gender-matched controls. Among six emotions, fearful expression recognition was specifically impaired in PDD subjects. Age had positive effects on fearful expression recognition directly and indirectly via the development of face recognition in controls, but not in PDD subjects. Furthermore, fearful expression recognition was related to the severity of PDD symptoms. We conclude that individuals with PDD show an atypical development of facial expression recognition. Moreover, impaired fearful expression recognition is closely related to social dysfunction.

Annotation: Development of facial expression recognition from childhood to adolescence: behavioural and neurological perspectives.

Intact emotion processing is critical for normal emotional development. Recent advances in neuroimaging have facilitated the examination of brain development, and have allowed for the exploration of the relationships between the development of emotion processing abilities, and that of associated neural systems. A literature review was performed of published studies examining the development of emotion expression recognition in normal children and psychiatric populations, and of the development of neural systems important for emotion processing. Few studies have explored the development of emotion expression recognition throughout childhood and adolescence. Behavioural studies suggest continued development throughout childhood and adolescence (reflected by accuracy scores and speed of processing), which varies according to the category of emotion displayed. Factors such as sex, socio-economic status, and verbal ability may also affect this development. Functional neuroimaging studies in adults highlight the role of the amygdala in emotion processing. Results of the few neuroimaging studies in children have focused on the role of the amygdala in the recognition of fearful expressions. Although results are inconsistent, they provide evidence throughout childhood and adolescence for the continued development of and sex differences in amygdalar function in response to fearful expressions. Studies exploring emotion expression recognition in psychiatric populations of children and adolescents suggest deficits that are specific to the type of disorder and to the emotion displayed. Results from behavioural and neuroimaging studies indicate continued development of emotion expression recognition and neural regions important for this process throughout childhood and adolescence. Methodological inconsistencies and disparate findings make any conclusion difficult, however. Further studies are required examining the relationship between the development of emotion expression recognition and that of underlying neural systems, in particular subcortical and prefrontal cortical structures. These will inform understanding of the neural bases of normal and abnormal emotional development, and aid the development of earlier interventions for children and adolescents with psychiatric disorders.