Due to their high reliability, security, and anti-counterfeiting, finger-based biometrics (such as finger knuckle print) have recently received considerable attention. Despite recent advances in finger-based biometrics, most of these approaches leverage much prior information and are non-robust for different modalities or different scenarios. To address this problem, we propose a structured Robust and Sparse Least Square Regression (RSLSR) framework to adaptively learn discriminative features for personal identification. To achieve the powerful representation capacity of the input data, RSLSR synchronously integrates robust projection learning, noise decomposition, and discriminant sparse representation into a unified learning framework. Specifically, RSLSR jointly learns the most discriminative information from the original pixels of the finger images by introducing the \(l_{2,1}\) norm. A sparse transformation matrix and reconstruction error are simultaneously enforced to enhance its robustness to noise, thus making RSLSR adaptable to multi-scenarios. Extensive experiments on five contact-based and contactless-based finger databases demonstrate the clear superiority of the proposed RSLSR in terms of recognition accuracy and computational efficiency.