원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.6
2014.12
pp.369-378
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this paper, based on the study of the Two-Dimensional Principal Component Analysis (2DPCA), Two-Dimensional Principal Component Analysis (2DPCA) and fuzzy set theory, we propose a integrated face recognition algorithm based on wavelet subspace. This method can make good use of the advantages of each single method, and also can make up for the defect of each other. The comparison of the results of the different methods identification effect on the ORL、YALE and FERET face database show, the integrated method proposed in this paper improves the recognition rate, and it also reduces the training and classification time as well.
목차
Abstract
1. Introduction
2．Describe about the Relevant Principles and Methods
2.1. Wavelet Transform
2.2. DPCA (Two--dimensional Principal Component Analysis) Method
2.3. Fuzzy 2DLDA (Two-dimensional Linear Discriminant Analysis) Method
3. The Design Principle and Structure Process of this Method
4. Experimental Results and Analysis
4.1. The Experimental Data and Description
4.2. The Experimental Environment Description
4.3. Comparison of Experiment Results
5. Conclusion
References
1. Introduction
2．Describe about the Relevant Principles and Methods
2.1. Wavelet Transform
2.2. DPCA (Two--dimensional Principal Component Analysis) Method
2.3. Fuzzy 2DLDA (Two-dimensional Linear Discriminant Analysis) Method
3. The Design Principle and Structure Process of this Method
4. Experimental Results and Analysis
4.1. The Experimental Data and Description
4.2. The Experimental Environment Description
4.3. Comparison of Experiment Results
5. Conclusion
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보