Component-Based cascade linear discriminant analysis for face recognition

Authors:
Wenchao Zhang;Shiguang Shan;Wen Gao;Yizheng Chang;Bo Cao
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P.R China;ICT-ISVISION Joint R&D Laboratory for Face Recognition, CAS, Beijing, P.R China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P.R China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P.R China;ICT-ISVISION Joint R&D Laboratory for Face Recognition, CAS, Beijing, P.R China
Venue:
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Year:
2004

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Abstract

This paper presents a novel face recognition method based on cascade Linear Discriminant Analysis (LDA) of the component-based face representation In the proposed method, a face image is represented as four components with overlap at the neighboring area rather than a whole face patch Firstly, LDA is conducted on the principal components of each component individually to extract component discriminant features Then, these features are further concatenated to undergo another LDA to extract the final face descriptor, which actually have assigned different weights to different component features Our experiments on the FERET face database have illustrated the effectiveness of the proposed method compared with the traditional Fisherface method both for face recognition and verification.