Two-directional two-dimensional random projection and its variations for face and palmprint recognition

Abstract

2DRP (two-dimensional random projection) is two-dimensional extension of one-dimensional RP (random projection) to keep biometric images from being reshaped to vectors before RP for recognition. We propose a novel method called (2D)2RP (two-directional two-dimensional random projection) for feature extraction of biometrics. (2D)2RP directly projects the image matrix from high-dimensional space to low-dimensional space to extract optimal projective vectors at row-direction and column-direction. (2D)2RP, similar to RP, can also avoid the problems of singularity, SSS (small sample size) and over-fitting; furthermore it has much less storage and computational cost than RP. Besides, the variations of (2D)2RP combined with 2DPCA and 2DLDA are developed. Experimental results and comparison discussion among (2D)2RP and its variations on face and palmprint databases confirm the performance and effectiveness of (2D)2RP and its variations. © 2011 Springer-Verlag.