Deep Learning Based Approach to Enhanced Recognition of Emotions and Behavioral Patterns of Autistic Children

TL;DR

This work tackles the challenge of recognizing emotions in autistic children under real-world, variable imaging conditions. It introduces an autoencoder-based preprocessing pipeline that standardizes variable-sized facial images to a fixed input while preserving emotional cues, enabling improved emotion classification by Xception and InceptionV3 networks. The approach yields substantial, statistically significant gains (over 13 percentage points in accuracy) with very large effect sizes, across architectures, and demonstrates robustness via tight confidence intervals and low variance. The findings suggest practical benefits for ASD interventions and indicate strong potential for clinical translation and broader applicability in affective computing tasks.

Abstract

Autism Spectrum Disorder significantly influences the communication abilities, learning processes, behavior, and social interactions of individuals. Although early intervention and customized educational strategies are critical to improving outcomes, there is a pivotal gap in understanding and addressing nuanced behavioral patterns and emotional identification in autistic children prior to skill development. This extended research delves into the foundational step of recognizing and mapping these patterns as a prerequisite to improving learning and soft skills. Using a longitudinal approach to monitor emotions and behaviors, this study aims to establish a baseline understanding of the unique needs and challenges faced by autistic students, particularly in the Information Technology domain, where opportunities are markedly limited. Through a detailed analysis of behavioral trends over time, we propose a targeted framework for developing applications and technical aids designed to meet these identified needs. Our research underscores the importance of a sequential and evidence-based intervention approach that prioritizes a deep understanding of each child's behavioral and emotional landscape as the basis for effective skill development. By shifting the focus toward early identification of behavioral patterns, we aim to foster a more inclusive and supportive learning environment that can significantly improve the educational and developmental trajectory of children with ASD.

Figures (5)

• Figure 1: Structure of a AutoEncorder
• Figure 2: Xception Model Structure
• Figure 3: InceptionV3 Model Structure
• Figure 4: Performance Comparison: Baseline vs Autoencoder-Enhanced Models.
• Figure 5: Statistical significance analysis showing very large effect sizes and substantial error rate reductions.