Fast and efficient minutia‐based palmprint matching

Abstract

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 secondlevel features, such as minutiae, which are more reliable in comparison with the first group. Owing to a large number of minutiae in a palmprint, ∼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.

Robust palmprint identification using efficient enhancement and two‐stage matching technique

“…To test the identification accuracy of the proposed algorithm, comparisons are carried out on publicly available high‐resolution Tsinghua palmprint database (THUPALMLAB) [28]. Recent state‐of‐the‐art high‐resolution palmprint identification works [3, 29, 30] have made use of this database to carry out their experimentations and for comparing their results with other studies. Therefore, in this study, we have followed the same experimental setup as these studies and used this database for experimentation.…”

Distribution of the minutiae in palmprints: Topological and sexual variability

“…To assess the association between the types of palm deltas and the area of location, a correspondence analysis was performed, which is shown in Figure 7 Furthermore, due to the large amount of minutiae, approximately ten times more than a fingerprint, the research process requires much more time [33,39]. It should also be stressed that the quality of palmprints is usually not as good as that of fingerprints, due both to the deformation and concave structure in the center of the palmprint, and to the greater number of wide creases, white lines and wrinkles, especially numerous in some palm areas, which means that, even in high-resolution palmprints, many pseudo-minutiae may be extracted and it is difficult to extract reliable minutiae effectively.…”

Palmprint image registration using convolutional neural networks and Hough transform

“…Despite the available algorithms, some problems with the palmprint matching still need to be solved for largescale applications [19]: skin distortion, diversity of different palm regions, and computational complexity. Registering palmprint images into the same coordinate, has been used to address the skin distortion and problem of large number of minutia comparison during the match process [19], [20]. By registration, it does not need to compare all minutia from two pamprint.…”

“…This helps to mitigate the effects of skin distortion and improve matching speed. However, the registration methods proposed in [19], [20] are not accurate enough, and also for algorithm in [19] there is no criterion to show whether the registration is successful.…”

“…To increase the matching speed and accuracy, motivated by [19] and our previous work [20], we developed a new palmprint registration method. In this method we combine CNN and GHT to register all palmprint images with a reference image.…”