Combined Contourlet and Non-subsampled Contourlet Transforms Based Approach for Personal Identification Using Palmprint

Palmprint based personal verification is an accepted biometric modality due to its reliability, ease of acquisition and user acceptance. This paper presents a novel palmprint based identification approach which draw on the textural information available on the palmprint by utilizing a combination of contourlet and non subsampled contourlet transforms. Center of the palm is computed using the distance transform whereas the parameters of best fitting ellipse help determine the alignment of the palmprint. ROI of 256×256 pixels is cropped around the center, and subsequently it is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed using contourlet and non subsampled contourlet transforms. The proposed algorithm captures global details in a palmprint as compact fixed length palm codes for contourlet and NSCT respectively which are further concatenated at feature level for identification using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 500 palm images of GPDS hand database, acquired from University of Las Palmas de Gran Canaria. The experimental results were compiled for individual transforms as well as for their optimized combination at feature level. CT based approach demonstrated the decidability index of 2.6212 and equal error rate (EER) of 0.7082% while NSCT based approach depicted decidability index of 2.7278 and EER of 0.5082%. The feature level fusion achieved decidability index of 2.7956 and EER of 0.3112%.