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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.
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).