원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.4 No.3
2011.09
pp.85-94
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The paper will present a novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis (2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the most powerful discriminative method for classification. Experiments based on proposed method have been conducted on two public data sets FERET and AT&T; the results show that the proposed method could improve the classification rates.
목차
1. Introduction
2. 2D Principal Component Analysis
2.1. Face Model Construction
2.2. Feature Extraction
3. Support Vector Machine
3.1. Classifier Construction Phase
3.2. Classification Phase
3.3. SVM for Face Identification
4. Implementation and Experiments
4.1. Experiments on AT&T database
4.2. Experiments on FERET Database
5. Conclusions
References
2. 2D Principal Component Analysis
2.1. Face Model Construction
2.2. Feature Extraction
3. Support Vector Machine
3.1. Classifier Construction Phase
3.2. Classification Phase
3.3. SVM for Face Identification
4. Implementation and Experiments
4.1. Experiments on AT&T database
4.2. Experiments on FERET Database
5. Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보