Year: 2025
Type: article
Abstract: Communication and social interaction issues are frequently linked to autism, which can have an impact on quality of life, work, and education. Opportunities to lessen these difficulties are presented by assistive technology, especially those that facilitate individualized and encouraging engagement. Facial expression recognition (FER) is essential to these systems, but current methods are still inadequate for autism-specific situations even though they achieve high accuracy on benchmark datasets like CK+. Because autistic people usually exhibit aberrant, subtle, or ambiguous facial expressions—which deviate from the common patterns used to train traditional FER models—this limitation occurs. In this work, we suggest a hybrid model that combines an LSTM network for temporal modeling of video sequences with three pretrained convolutional neural networks (EfficientNetB0, ResNet50, and MobileNetV2) for spatial feature extraction. Although the model performs well on CK+, its applicability to autism is still limited by the lack of relevant datasets and the use of artificial intelligence (AI)-generated videos rather than authentic recordings. The critical need for more comprehensive data and adaptive model designs tailored to autistic populations is highlighted by these findings.
Institution: Mohammed V University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4414779752