A palmprint recognition algorithm using Principal Component Analysis of phase information

This paper presents a palmprint recognition algorithm using principal component analysis (PCA) of phase information in 2D (two-dimensional) discrete Fourier transforms (DFTs) of palmprint images. To achieve highly robust palmprint recognition, the proposed algorithm (i) limits the frequency bandwidth, and (ii) averages phase spectra using multiple palmprint images captured from the same hand at an enrollment stage. Through a set of experiments, we demonstrate that the proposed method can significantly reduce computational cost without sacrificing recognition performance compared with our previous work using phase-only correlation (POC) - an image matching technique using the phase components in 2D DFTs of given images. Also, the resulting performance is much higher than those of conventional palmprint recognition algorithms which apply PCA to palmprint images, or phase spectra directly.