## 7 Discussion and Analysis
### Limitations and Future Work  
Despite the promising results achieved in this study, several limitations should be acknowledged that point toward directions for future research as follows:  
1. Error Patterns and Performance Variations  
Despite achieving 90.73% overall accuracy, our model demonstrates systematic error patterns that warrant critical examination. Analysis reveals confusion primarily between calm and sad emotions (approximately 4.6% bidirectional misclassification), while angry emotions have the lowest recognition rate (88.85%) and happy emotions the highest(93.02%). These performance discrepancies reflect fundamental challenges in emotion recognition for children with autism rather than mere algorithmic limitations. The calm-sad confusion pattern likely stems from the documented \"flat affect\" characteristic in autism spectrum disorders, where similar facial configurations may represent different internal emotional states. This inherent ambiguity presents a theoretical challenge that transcends technical solutions, suggesting potential limitations in discrete emotion classification approaches for this population. Similarly, the lower recognition rate for angry emotions may reflect the heterogeneous manifestation of negative emotional states in autism, where expressions may range from subtle microexpressions to pronounced but atypical configurations. Our subject-level analysis reveals substantial performance variations across individual participants, with recognition accuracies ranging from 82.4% to 97.1%. Lower performance was consistently observed for minimally verbal participants and those with more severe autism symptoms. This variability indicates that autism severity and communication ability significantly influence emotional expression detectability, a critical factor not explicitly accounted for in our current model architecture. Future work should explore adaptation mechanisms that can accommodate this individual heterogeneity, potentially incorporating autism severity metrics as model parameters to enable more personalized emotion recognition. Dataset Limitations  
The dataset, comprising data from only 33 children at a single educational facility in East China, introduces significant constraints on the generalizability of our findings, despite our basic data augmentation efforts through horizontal flipping and Gaussian blurring. This regional concentration raises critical questions about potential cultural influences on emotional expression patterns. Research demonstrates cultural variations in emotional display rules and expression intensity, potentially limiting our model's applicability across diverse populations. Additionally, our dataset concerns children primarily aged 4-5 years, neglecting developmental trajectories in emotional expression across the lifespan of individuals with autism. The relatively small sample size (33 participants) provides insufficient representation of the heterogeneity within autism spectrum disorders, particularly regarding symptom severity, verbal ability, and comorbid conditions. While we implemented careful subject separation during data division to prevent data leakage, our current augmentation approach addresses only a narrow range of potential variations. Future work should implement more comprehensive augmentation techniques including random rotations to simulate natural head movements, brightness and contrast adjustments for lighting robustness, and advanced methods such as mixup augmentation or generative approaches to create synthetic training examples. For speech data, time stretching, pitch shifting, and random time masking could further diversify the training samples and better account for the spectrum of communication variations in autism. The class imbalance in our dataset (calm emotions over-represented at 31.7% compared to happy emotions at 19.6%) may bias the model toward better recognition of more frequently encountered emotional states, contributing to the observed performance disparities between emotion categories.