Technology-assisted emotion recognition for autism spectrum disorder (ASD) children: a systematic literature review

The information about affective states in individuals with autism spectrum disorder (ASD) is difficult to obtain as they usually suffer from deficits in facial expression. Affective state conditions of individuals with ASD were associated with impaired regulation of speech, communication, and social skills leading towards poor socio-emotion interaction. It is conceivable that the advance of technology could offer a psychophysiological alternative modality, particularly useful in persons who cannot verbally communicate their emotions as affective states such as individuals with ASD. The study is focusing on the investigation of technology-assisted approach and its relationship to affective states recognition. A systematic review was executed to summarize relevant research that involved technology-assisted implementation to identify the affective states of individuals with ASD using Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) approach. The output from the online search process obtained from six publication databases on relevant studies published up to 31 July 2020 was analyzed. Out of 391 publications retrieved, 20 papers met the inclusion and exclusion criteria set in prior. Data were synthesized narratively despite methodological and heterogeneity variations. In this review, some research methods, systems, equipment and models to address all the related issues to the technology-assisted and affective states concerned were presented. As for the consequence, it can be assumed that the emotion recognition with assisted by technology, for evaluating and classifying affective states could help to improve efficacy in therapy sessions between therapists and individuals with ASD. This review will serve as a concise reference for providing general overviews of the current state-of-the-art studies in this area for practitioners, as well as for experienced researchers who are searching for a new direction for future works

Understanding of facial features in face perception: insights from deep convolutional neural networks

IntroductionFace recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process.MethodsTo address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees.ResultsOur findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN.DiscussionOverall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field