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Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and
Jingying Chen1, Chang Chen1, Ruyi Xu2
1Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Children (Basel, Switzerland)
|November 27, 2024
Summary
This study introduces an advanced facial behavior analysis model for autism identification, achieving 88.74% accuracy. The model integrates coarse and fine-grained analyses, improving interpretability and generalizability for autism diagnosis.
Area of Science:
- Biomedical Engineering
- Computer Science
- Developmental Neuroscience
Background:
- Facial behavior is a key biomarker for autism identification.
- Individual differences in autism present challenges for traditional methods.
- Deep learning models lack interpretability.
Purpose of the Study:
- To develop an interpretable facial behavior characterization model for autism identification.
- To address limitations of traditional and deep learning methods.
Main Methods:
- Integrated coarse-grained (statistical measures) and fine-grained (LSTM for temporal dynamics) facial behavior analysis.
- Employed a feature-level attention mechanism for enhanced interpretability and feature highlighting.
Main Results:
- Achieved an average recognition accuracy of 88.74% on a self-constructed autism dataset.
- Outperformed standalone coarse-grained analysis by 8.49%.
Conclusions:
- The integrated approach improves generalizability of facial behavior features.
- Effectively mitigates challenges from intragroup variability in autism.
- Contributes to more accurate and interpretable autism identification.
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