2D and 3D palmprint information, PCA and HMM for an improved person recognition performance
Integrated Computer-Aided Engineering
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As a significant biometric technique, 3D palm print authentication is better than 2D palm print authentication in several aspects. Previous work on 3D palm print recognition has concentrated on two aspects: (1) extracting the texture and line features using the binary image of 3D palm print, (2) extracting the orientation features using the Gabor filter and competitive code. In this paper we extract, for the first time, the 3D palm print features using the appearance-based linear discriminant analysis (LDA) method. The appearance-based LDA method can extract the global algebraic features of the biometrics. These features have been proven to have strong discriminability. We also investigated the relationship between the recognition accuracy and the resolution of the 3D palm print image. The experimental results show that the 3D palm print images with resolution and are better for 3D palm print recognition. The experiment results also show the feasibility of our method.