Touchless-based fingerprint recognition technology is thought to be an alternative to touch-based systems to solve problems of hygienic, latent fingerprints, and maintenance. However, there are few studies about touchless fingerprint recognition systems due to the lack of a large database and the intrinsic drawback of low ridge-valley contrast of touchless fingerprint images. This Chapter proposes an end-to-end solution for user authentication systems based on touchless fingerprint images in which a multi-view strategy is adopted to collect images and the robust fingerprint feature of touchless image is extracted for matching with high recognition accuracy. More specifically, a touchless multi-view fingerprint capture device is designed to generate three views of raw images followed by preprocessing steps including region of interest (ROI) extraction and image correction. The distal interphalangeal crease (DIP)-based feature is then extracted and matched to recognize the human’s identity in which part selection is introduced to improve matching efficiency. Experiments are conducted on two sessions of touchless multi-view fingerprint image database with 541 fingers acquired about 2 weeks apart. An EER of 1.7% can be achieved by using the proposed DIP-based feature, which is much better than touchless fingerprint recognition by using scale invariant feature transformation (SIFT) and minutiae features. The given fusion results show that it is effective to combine the DIP-based feature, minutiae, and SIFT feature for touchless fingerprint recognition systems. The EER is as low as 0.5%.