Robust and adaptive region of interest extraction for unconstrained palmprint recognition

Palmprint region of interest (ROI) extraction under unconstrained conditions is an unavoidable problem in realizing palmprint recognition. However, the diversity of palm size, posture, illumination, and background undoubtedly poses a great challenge. Therefore, in response to this problem, we collected about 30,000 unconstrained palmprint images from 100 people using five mobile phones with the flash on and off and then manually annotated the 14 key points of each image, which constitutes the most challenging palmprint database at present, namely XJTU-UP. In addition, we propose a method for palmprint ROI extraction. First, palm detection is performed to remove irrelevant background, then detect the key points, and finally establish a coordinate system based on the obtained points to extract the ROI. In the process of key points detection, an auxiliary network and data imbalance functionality are introduced to improve the accuracy. Finally, the experimental results on the XJTU-UP database show that the recognition accuracy has a maximum increase of 2.16% and the true acceptance rate is improved by up to 20.56% when the false acceptance rate is 0.01% compared to suboptimal method.