The usage of touchless palmprint recognition increases due to its acceptant interaction mode. However, the region of interest localization at a distance is quite challenging for touchless palmprint recognition in real-world scenarios with complex backgrounds and hand poses. To address this issue, this chapter proposes a palm keypoint localization neural network (PKLNet) that combines information on the hand region, palm boundary, and finger valley edges to achieve accurate and robust keypoint localization. First, a two-stage neural network is proposed. It effectively adopted the transformer framework to capture global relations of the palm boundary points to perform palm region segmentation and ROI keypoint coordinate regression. Second, an image synthesis-based training strategy is developed based on conventional palmprint ROI localization methods. The obtained ROI localizer (namely PalmKit) can automatically generate palm region masks and keypoint coordinates, which can significantly simplify the data annotation process and hence liberate the heavy manual labor. Finally, extensive experiments are performed on various touchless palmprint datasets. The proposed PKLNet obtains an 82.1% success rate and a 7.7 pixels median localization error in the cross-dataset test. The results demonstrate that the proposed PKLNet is robust to palm rotation, translation, and inference from complex backgrounds, ensuring the usability of the touchless palmprint recognition technique in real-world application scenarios.