Journal
COGNITIVE COMPUTATION
Volume 8, Issue 5, Pages 900-909Publisher
SPRINGER
DOI: 10.1007/s12559-016-9403-y
Keywords
Face recognition with image sets; Manifold learning; Multi-manifolds discriminative canonical correlation analysis; Dimensionality reduction
Funding
- National Science Foundation of China [60802069, 61273270]
- Natural Science Foundation of Guangdong Province [2014A030313173]
- Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund
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In this paper, multi-manifolds discriminative canonical correlation analysis (MMDCCA) is presented for solving face recognition problem with using different image sets. We adopt Linearity-Constrained Hierarchical Agglomerative Clustering algorithm for dividing all image sets into a range of local clusters. Then MMDCCA is proposed to find multiple orthogonal projection functions for maximizing the margins of manifolds with different persons. In order to obtain gains in discrimination accuracy, we enforce a constraint that each person-specific manifold is orthogonal to those of all other manifolds after linear transformation. An efficient sequential iterative learning algorithm is used for finding the discriminative features. Extensive experiments confirm the effectiveness of our model.
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