Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Should We RepresentFaces for Automatic Recognition?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple hybrid classifier for face recognition with adaptively generated virtual data
Pattern Recognition Letters
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Synthetic on-line signature generation. Part I: Methodology and algorithms
Pattern Recognition
A novel hand reconstruction approach and its application to vulnerability assessment
Information Sciences: an International Journal
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In face recognition system, achieving higher recognition rate by limited training images is a challenging problem. An idea to solve this problem is generalizing some virtual probes from original probes such as nearest feature line (NFL) [IEEE Trans. Neural Networks 10 (1999) 439], nearest feature angle (NFA) and simple hybrid classier (SHC) (hybrid NFA and NFL) method [Pattern Recognition Lett. 23 (2002) 833].In this paper, an improved method for generalizing probe sets called linear generalization subspace (LGS) is proposed, in which the generalized area is some constrained linear subspaces of the original probes. In LGS, a method called constraint least square residual distance is suggested in this paper. Experimental results show that the proposed method has lower recognition error rate than the nearest neighbor method (without generalization), NFL and SHC, respectively, and low computation complexity than NFL or SHC methods based on ORL face database and a larger combination face database.