Palmprint recognition has been widely applied in security and, particularly, authentication. In the past decade, various palmprint recognition methods have been proposed and achieved promising recognition performance. However, most of these methods require rich a priori knowledge and cannot adapt well to different palmprint recognition scenarios, including contact-based, contactless, and multispectral palmprint recognition. This problem limits the application and popularization of palmprint recognition. In this article, motivated by the least square regression, we propose a salient and discriminative descriptor learning method (SDDLM) for general scenario palmprint recognition. Different from the conventional palmprint feature extraction methods, the SDDLM jointly learns noise and salient information from the pixels of palmprint images, simultaneously. The learned noise enforces the projection matrix to learn salient and discriminative features from each palmprint sample. Thus, the SDDLM can be adaptive to multiscenarios. Experiments were conducted on the IITD, CASIA, GPDS, PolyU near infrared (NIR), noisy IITD, and noisy GPDS palmprint databases, and palm vein and dorsal hand vein databases. It can be seen from the experimental results that the proposed SDDLM consistently outperformed the classical palmprint recognition methods and state-of-the-art methods for palmprint recognition.