Hidden Markov models & principal component analysis for multispectral palmprint identification

Automatic personal identification from their physical and behavioral traits, called biometrics technologies, is now needed in many fields such as: surveillance systems, access control systems, physical buildings and many more applications. In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint images (MSP) using Hidden Markov Model (HMM) and Principal Components Analysis (PCA). In this study, the band image {RED, BLUE, GREEN and Nearest-InfraRed (NIR)} is rotated with different orientations then applying the PCA technique to each oriented image, to decorrelate the image columns, and concentrate the information content on the first components of the transformed vectors. Thus, the observation vector is formed by concatenate some components of the transformed vectors for all orientations. Subsequently, we use the HMM for modeling the observation vector of each MSP. Finally, log-likelihood scores are used for MSP matching. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification and accuracy rate.