Maximum variance projections for face recognition

Tianhao Zhang; Jie Yang; Huahua Wang; Chunhua Du

doi:10.1117/1.2746880

1 June 2007 Maximum variance projections for face recognition

Tianhao Zhang, Jie Yang, Huahua Wang, Chunhua Du

Author Affiliations +

Optical Engineering, Vol. 46, Issue 6, 067206 (June 2007). https://doi.org/10.1117/1.2746880

Abstract

Maximum variance projection (MVP), as a novel subspace learning algorithm, is proposed. It is a linear discriminant algorithm that preserves local information by capturing the local geometry of the manifold. Two abilities of manifold learning and classification are combined into the properties of our algorithm. Since face images often belong to a submanifold of intrinsically low dimension, we carry out the MVP algorithm for face manifold learning and classification. Several experiments show the effectiveness of our developed algorithm.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Tianhao Zhang, Jie Yang, Huahua Wang, and Chunhua Du "Maximum variance projections for face recognition," Optical Engineering 46(6), 067206 (1 June 2007). https://doi.org/10.1117/1.2746880

Published: 1 June 2007

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