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A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism
Shoujun Tang1, Mohammad Shabaz2
1Guangdong Polytechnic Institute, The Open University of Guangdong, Guangzhou 510091, China.
Journal of Healthcare Engineering
|July 9, 2021
Summary
This study introduces an enhanced cerebellum-basal ganglia mechanism (CBGM) for improved face recognition. The new method achieves high accuracy, even under challenging illumination conditions, outperforming conventional techniques.
Area of Science:
- Computer Vision
- Biometric Recognition
- Artificial Intelligence
Background:
- Face recognition is crucial for security and identification systems.
- Illumination variations present a significant challenge for accurate face recognition.
- Existing methods like nonsubsampled contourlet transform (NSCT) lack sufficient accuracy for similar faces.
Purpose of the Study:
- To propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for robust face recognition.
- To address the limitations of conventional algorithms in handling illumination variations.
- To improve the accuracy and reliability of face identification systems.
Main Methods:
- Utilizing an enhanced cerebellum-basal ganglia mechanism (CBGM) for feature extraction.
- Employing integral projection and geometric feature assortment for acquiring facial image features.
- Developing a cognition model based on the CBGM for enhanced feature extraction.
Main Results:
- The enhanced CBGM algorithm demonstrates effective face image recognition capabilities.
- Achieved a high recognition accuracy rate of 96.9% on 100 AR face images.
- The proposed CBGM technique significantly improves recognition accuracy compared to conventional methods.
Conclusions:
- The enhanced CBGM provides a robust solution for face recognition under varying illumination.
- The integral projection and geometric feature assortment effectively capture facial features.
- The CBGM-based cognition model offers superior performance in face identification.
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