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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2013.11
- 수록면
- 1,338 - 1,347 (10page)
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초록· 키워드
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Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher"s linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.
목차
ABSTRACT
1. INTRODUCTION
2. Dimension Reduction and Feature Extraction
3. The Structure of Fuzzy RBF Neural Network
4. Learning Algorithm of Fuzzy RBF Neural Network
5. Experimental Results
6. CONCLUSION
REFERENCE
참고문헌 (22)
참고문헌 신청
1. [학술지(정기간행물)] - R. Chellappa / 1995 / Human and Machine Recognition of Faces: A Survey / Proc. the IEEE 83 (5) : 705 ~ 740
2. [학술지(정기간행물)] - M. Turk / 1991 / Eigenfaces for Recognition / Journal of Cognitive Neuroscience 3 (1) : 71 ~ 86
3. [보고서] - Tat-Jun Chin / 2004 / A Study of the Eigenface Approach for Face Recognition : 1 ~ 18
4. [보고서] - D. Blackburn / 2000 / Face Recognition Vendor Test 2000: Evaluation Report
5. [학술지(정기간행물)] - P.J. Phillips / 2000 / FERET Evaluation Methodology for Face Recognition Algorithms / IEEE Transaction on Pattern Analysis and Machine Intelligence 22 (10) : 1090 ~ 1103
2. [학술지(정기간행물)] - M. Turk / 1991 / Eigenfaces for Recognition / Journal of Cognitive Neuroscience 3 (1) : 71 ~ 86
3. [보고서] - Tat-Jun Chin / 2004 / A Study of the Eigenface Approach for Face Recognition : 1 ~ 18
4. [보고서] - D. Blackburn / 2000 / Face Recognition Vendor Test 2000: Evaluation Report
5. [학술지(정기간행물)] - P.J. Phillips / 2000 / FERET Evaluation Methodology for Face Recognition Algorithms / IEEE Transaction on Pattern Analysis and Machine Intelligence 22 (10) : 1090 ~ 1103
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UCI(KEPA) : I410-ECN-0101-2015-004-000990348