Title :
SVM-based Discriminant Analysis for face recognition
Author :
Kim, Sang-Ki ; Toh, Kar-Ann ; Lee, Sangyoun
Author_Institution :
Dept. of Electr. & Electron. Eng., Yonsei Univ., Seoul
Abstract :
In this paper, we introduce a novel variant of Linear Discriminant Analysis (LDA) for face recognition. The proposed method attempts to find an optimal LDA matrix by redesigning the between-class scatter matrix incorporating a Support Vector Machine (SVM). Our empirical evaluations show that the proposed method offers noticeable performance improvement over the conventional LDA.
Keywords :
face recognition; matrix algebra; support vector machines; SVM-based discriminant analysis; face recognition; linear discriminant analysis; optimal LDA matrix; support vector machine; Biometrics; Eigenvalues and eigenfunctions; Face recognition; Feature extraction; Independent component analysis; Kernel; Linear discriminant analysis; Principal component analysis; Scattering; Support vector machines;
Conference_Titel :
Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1717-9
Electronic_ISBN :
978-1-4244-1718-6
DOI :
10.1109/ICIEA.2008.4582892
