Extracting Palmprint ROI From Whole Hand Image Using Straight Line Clusters

This paper proposes a novel method to extract a palmprint region of interest (ROI) from whole hand images using straight line clusters. The core idea of our method is that the fingers can be easily detected by straight line clusters due to the distinctive appearance of the fingers. In our method, the original whole hand image is first converted to a binary image. In the binary image, we draw many straight lines according to several predefined rules. For one straight line, if there are eight intersection points between this straight line and the hand region, we can conclude that this line passes through four fingers. In this case, it is easy to know the positions of finger joint areas. Then, key point candidates can be further detected in these finger joint areas. In our method, we draw many straight lines to detect finger joint areas, which may result in detecting several different key point candidates in one finger joint area. Thus, a lot of key point candidates may be obtained in four finger joint areas. We then exploit the k-means clustering algorithm to calculate four cluster centers, which are treated as the final four key points. Furthermore, utilizing the distance information among four key points, we can know the position order of four key points. The final key points can be used to construct a coordinate system. In this new coordinate system, after rotation normalization, the ROI can be extracted from the central region of hand. We also collected a database including 16 000 whole hand images. The experimental results demonstrate that the proposed method can achieve 100% localization and extraction accuracy.