**Abstract**

So far, there exist many publicly available palmprint databases. However, not all of them have provided the corresponding region of interest (ROI) images. If everyone uses their own extracted ROI images for performance testing, the final accuracy is not strictly comparable. Since ROI localization is the critical stage of palmprint recognition. The location precision has a significant impact on the final recognition accuracy, especially in unconstrained scenarios. This problem has limited the applications of palmprint recognition. However, many currently published surveys only focus on feature extraction and classification methods. Throughout these years, many new ROI localization methods have been proposed. In this chapter, we will group the existing ROI localization methods into different categories, analyze their basic ideas, reproduce some of the codes, make comparisons of their performances, and provide further directions. We hope this could be a useful reference for further research.

**Keywords:** biometrics, palmprint recognition, palmprint database, region of interest localization, palm region segmentation

### **1. Introduction**

Palm-related biometrics can easily reach high accuracy due to two reasons. One is that palmprint contains plenty of features, such as principal lines, wrinkles, ridges and valleys, and minutiae points; another one is that the regions of interest (ROIs) could be aligned with the help of the finger valley points. Since the captured palms may have different rotations and scales, to obtain high accuracy, the extracted palmprint images should be aligned with each other. It means the palmprint region should be localized based on the relative coordinate system, which is established basing on the keypoints of the finger valleys. Most of the current palmprint recognition algorithms are based on the direction information of the palmprint lines and textures [1, 2]. Hence, misalignment will significantly affect the final matching score. A robust and precise ROI localization method is essential for palmprint recognition, especially for touchless applications. Many organizations have collected their palmprint databases based on different research targets. More and more novel databases arise in recent years; some of them are captured across different devices, some with different illuminations, and some at different distances.

In the following section, we will review the current palmprint databases and ROI localization methods.
