Fast and Efficient Minutia-Based Palmprint Matching.

Using the palmprint in recognition systems has received a lot of interest during the last two decades. Some of these systems are based on first-level features, such as the existing lines and creases in palmprint images, and others use second-level features, such as minutiae, which are more reliable in comparison with the first group. Owing to a large number of minutiae in a palmprint, similar to 1000 minutiae, the matching process is time consuming. In this study, a new minutia-based matching strategy is proposed to make the matching process faster and more efficient. First, an orientation field estimation algorithm based on region-growing is proposed, which emphasises selecting seed points with higher quality. Second, the estimated orientation field is used to align palmprint images to the same coordinate system, resulting in fewer computations during minutia matching. Finally, a new minutia descriptor based on the orientation field is designed to distinguish minutiae with different local orientation structures. This descriptor helps to find two mated minutiae much faster, speeding up the matching process. The proposed palmprint matching algorithm has been evaluated on the THUPALMLAB database, and the results show the superiority of the proposed algorithm over most of the state-of-the-art algorithms.