Directional energy based palmprint identification using Non Subsampled Contourlet Transform

Palmprint based personal verification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a novel palmprint based identification approach which uses the textural information available on the palmprint by employing the non subsampled contourlet transform (NSCT). After establishing the region of interest (ROI), the two dimensional (2-D) spectrum is divided into fine slices, using iterated directional filter banks. Next, directional energy component for each block from the decomposed subband outputs is computed. The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. Palmprint matching is then performed using normalized Euclidean distance classifier. The algorithm is tested on a total of 7752 palm images, acquired from the standard database of Polytechnic University of Hong Kong. The experimental results demonstrated the feasibility of the proposed system by exhibiting Decidability Index of 2.8125 and equal error rate of 0.1604%, better than the reported techniques in literature.