Palmprint Recognition by using Bandlet, Ridgelet, Wavelet and Neural Network

Palmprint recognition has emerged as a substantial biometric based personal identification. Tow types of biometrics palmprint feature. high resolution feature that includes: minutia points, ridges and singular points that could be extracted for forensic applications. Moreover, low resolution feature such as wrinkles and principal lines which could be extracted for commercial applications. This paper uses 700nm spectral band PolyU hyperspectral palmprint database. Multiscale image transform: bandlet, ridgelet and 2D discrete wavelet have been applied to extract feature. The size of features are reduced by using principle component analysis and linear discriminate analysis. Feed-forward Back-propagation neural network is used as a classifier. The recognition rate accuracy shows that bandlet transform outperforms others

Fourier spectral of PalmCode as descriptor for palmprint recognition

Study on automatic person recognition by palmprint is currently a hot topic. In this paper, we propose a novel palmprint recognition method by transforming the typical palmprint phase code feature into its Fourier frequency domain. The resulting real-valued Fourier spectral features are further processed by horizontal and vertical 2DPCA method, which proves highly efficient in terms of computational complexity, storage requirement and recognition accuracy. This paper also gives a contrast study on palm code and competitive code under the proposed feature extraction framework. Besides, experimental results on the Hongkong PolyU Palmprint database demonstrate that the proposed method outperforms many currently reported local Gabor pattern approaches for palmprint recognition