Extraction of palmprint texture features using combined DWT-DCT and local binary pattern

Abstract

The palm-print verification system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the palm-print features are acquired from the sensor and stored in a database along with the person's identity for the recognition of his/her identity. In the recognition mode, the palm-print features are re-acquired from the sensor and compared against the stored data to determine the user identity. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of palm. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the palm is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed. Twenty hand-scanned images and ten samples of CASIA palmprint database were used in this experiment. The performance of the proposed system is found to be satisfactory.