Palmprint Identification Using 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 Contourlet Transform (CT). After establishing the region of interest (ROI), the two dimensional (2-D) spectrums is divided into fine slices, using iterated directional filterbanks. 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 proposed 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 genuine acceptance rate of 88.91%, decidability index of 2.7748 and equal ierror rate of 0.2333%.