Ensemble-Based discriminant manifold learning for face recognition

Authors:
Junping Zhang;Li He;Zhi-Hua Zhou
Affiliations:
Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, Shanghai, China;Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, Shanghai, China;National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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Abstract

The locally linear embedding (LLE) algorithm can be used to discover a low-dimensional subspace from face manifolds. However, it does not mean that a good accuracy can be obtained when classifiers work under the subspace. Based on the proposed ULLELDA (Unified LLE and linear discriminant analysis) algorithm, an ensemble version of the ULLELDA (En-ULLELDA) is proposed by perturbing the neighbor factors of the LLE algorithm. Here many component learners are generated, each of which produces a single face subspace through some neighborhood parameter of the ULLELDA algorithm and is trained by a classifier. The classification results of these component learners are then combined through majority voting to produce the final prediction. Experiments on several face databases show the promising of the En-ULLELDA algorithm.