Kernel discriminant analysis of Block-wise Gaussian Derivative Phase Pattern Histogram for palmprint recognition

This paper presents an efficient palmprint recognition technique for palmprints collected with visible as well as multispectral imaging system. ROI extraction is a challenging task for palmprint captured in unconstrained environment. ROI extracted by gaps between fingers and width of palm makes system rotation and translation invariant. Approximation ROI obtained by First-level decomposition of ROI using Haar wavelet reduces computational overhead as well as noise. Phase quantization of AROI by Gaussian derivative filter gives Gaussian derivative phase pattern image and its block-wise histograms are concatenated to form a single vector referred as BGDPPH descriptor. Dimension reduction is performed by increasing discrimination between genuine and impostor scores using chi-RBF kernel discriminant analysis (KDA). Weighted score level fusion of spectral palmprints on Fisher criterion improves recognition rate. Robustness of the proposed BGDPPH descriptor against blur and noise is evaluated on four gray-scale and two multispectral palmprint databases collected through touch-based and touch-less acquisition devices.