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Facial emotion recognition system for autistic children: a feasible study based on FPGA implementation
1School of Computer Engineering, Nanyang Technological University, Singapore, Singapore. smitha@ntu.edu.sg.
This study developed a portable facial emotion recognition system for autistic children. It uses Principal Component Analysis (PCA) on an FPGA, achieving 82.3% accuracy for real-time communication support.
Area of Science:
- Computer Science
- Artificial Intelligence
- Biomedical Engineering
Background:
- Autistic children struggle with interpreting facial emotions, impacting social interaction.
- Existing facial emotion recognition algorithms lack portability, limiting real-time applications.
Purpose of the Study:
- To develop a portable facial emotion recognition system for autistic children.
- To evaluate the feasibility of serial vs. parallel Principal Component Analysis (PCA) for hardware implementation.
Main Methods:
- Implemented serial and parallel PCA algorithms on a Virtex 7 FPGA.
- Focused on feature extraction for emotion recognition in facial expressions.
Main Results:
- Achieved 82.3% detection accuracy with an 8-bit word length.
- Evaluated the performance of different PCA implementations on the FPGA.
Conclusions:
- A portable, FPGA-based emotion recognition system is feasible for autistic children.
- PCA is a viable and efficient algorithm for hardware implementation in portable emotion detection devices.
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