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Journal of Information Science and Engineering, Vol. 26 No. 4, pp. 1525-1537 (July 2010)

Face Recognition Using L-Fisherfaces*

CHENG-YUAN ZHANG1,2 AND QIU-QI RUAN1
1Institute of Information Science
Beijing Jiaotong University
Beijing, 100044 China
2College of Information and Electrical Engineering
Shandong University of Science and Technology
Qingdao, 266510 China

An appearance-based face recognition approach called the L-Fisherfaces is proposed in this paper, By using Local Fisher Discriminant Embedding (LFDE), the face images are mapped into a face subspace for analysis. Different from Linear Discriminant Analysis (LDA), which effectively sees only the Euclidean structure of face space, LFDE finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. Different from Locality Preserving Projections (LPP) and Unsupervised Discriminant projections (UDP), which ignore the class label information, LFDE searches for the project axes on which the data points of different classes are far from each other while requiring data points of the same class to be close to each other. We compare the proposed L-Fisherfaces approach with PCA, LDA, LPP, and UDP on three different face databases. Experimental results suggest that the proposed L-Fisherfaces provides a better representation and achieves higher accuracy in face recognition.

Keywords: face recognition, local Fisher discriminant embedding, manifold learning, locality preserving projections, unsupervised discriminant projections

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Received July 29, 2008; revised October 30, 2008; accepted January 8, 2009.
Communicated by H. Y. Mark Liao. * This work is partially supported by the National Natural Science Foundation of China (NSFC, No. 60672062) and the Major State Basic Research Development Program of China (973 Program No. 2004CB318005).