A circle-based Region-Of-Interest segmentation method for palmprint recognition

This paper presents a novel method for optimal Region-Of-Interest (ROI) segmentation for palmprint feature extraction based on the largest inscribed circle. This ensures that the optimal amount of features can be extracted, unlike square-based methods which exclude a substantial area on the outside region of the palmprint image. After position normalization, the middle portion of the palmprint is searched to determine the center from which the largest inscribed circle can be extracted. The circular area is then unwrapped into a fixed-size rectangular strip which is further preprocessed to remove redundancies and then split into seven equal square sub-images. A layered approach is then adopted during the matching stage where each square is successively matched and polled to produce a matching score. Experiments are performed using the ‘PolyU Palmprint Database’ and results show that this is a viable method for palmprint feature extraction, with a recognition rate of above 90% obtained.