Development of a Block-based Palmprint Recognition Methodology Using the Discrete Cosine Transform

Hand-based biometric applications based on the palmprint have recently become a popular choice due to their high acceptance by the general public. In this paper, a method is presented which applies the type-II Discrete Cosine Transform (DCT) in a novel way by extracting selected coefficients from small overlapping blocks of scanned, medium-resolution palmprint images to form a feature vector for personal identification. The middle palm area is extracted after rotation, position and illumination normalization during the preprocessing stage before dividing it into 8×8 pixel blocks. The type-II DCT is then used to transform the blocks, and only a small set of the coefficients selected from each block after ranking based on magnitude only. The selected coefficients are concatenated to create a compact feature vector that represents each palmprint. Experiments using a subset of the PolyU Palmprint Database show that this block-based approach effectively discriminates between palmprints, with excellent palmprint recognition results and an Equal Error Rate (EER) of less than 2% in palmprint authentication when using a 64×64 pixel input image block.