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Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization
Shahenda Sarhan1,2, Aida A Nasr3, Mahmoud Y Shams3
1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Computational Intelligence and Neuroscience
|October 8, 2020
Summary
This study introduces a novel combined adaptive deep learning vector quantization (CADLVQ) classifier for multipose face recognition. The new method enhances accuracy and robustness in security applications.
Area of Science:
- Computer Science
- Artificial Intelligence
- Biometrics
Background:
- Multipose face recognition is a significant challenge in security applications.
- Existing research focuses on improving face detectors or recognition systems like Support Vector Machines and Deep Convolutional Neural Networks.
- Limitations exist in current multipose face recognition techniques, necessitating advanced solutions.
Purpose of the Study:
- To propose a novel combined adaptive deep learning vector quantization (CADLVQ) classifier for multipose face recognition.
- To enhance the performance of adaptive deep learning vector quantization classifiers by integrating majority voting and Speeded Up Robust Features.
- To evaluate the proposed CADLVQ classifier's effectiveness in multipose face recognition tasks.
Main Methods:
- A combined adaptive deep learning vector quantization (CADLVQ) classifier was developed.
- The majority voting algorithm was integrated with the Speeded Up Robust Feature (SURF) extractor to address weaknesses in existing adaptive deep learning vector quantization classifiers.
- Performance was evaluated using metrics such as sensitivity, specificity, precision, and accuracy.
Main Results:
- The proposed CADLVQ classifier demonstrated promising results in sensitivity, specificity, precision, and accuracy.
- Experimental results showed superior performance compared to recent approaches in deep learning, statistical, and classical neural networks.
- The confusion matrix analysis confirmed the reliability and robustness of the proposed system.
Conclusions:
- The combined adaptive deep learning vector quantization (CADLVQ) classifier offers a significant advancement for multipose face recognition systems.
- The integration of majority voting and SURF features effectively boosts classifier performance.
- The proposed system represents a robust and reliable state-of-the-art solution for multipose face recognition in security applications.
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