Palmprint recognition has been widely used in security authentication. However, most of the existing palmprint representation methods are focused on a special application scenario using the hand-crafted features from a single-view. If the features become weak as the application scenario changes, the recognition performance will be degraded. To address this problem, we propose to comprehensively exploit palmprint features from multiple views to improve the recognition performance in generic scenarios. In this paper, a novel double-cohesion learning based multiview and discriminant palmprint recognition (DC_MDPR) method is proposed, which imposes a double-cohesion strategy to reduce the inter-view margins for each subject and the intra-class margins for each view. In this way, for each subject, the features from different views can be closer to each other in the binary-label space. Meanwhile, for each view, the features sharing the same label information can move towards each other by imposing a neighbor graph regularization. The proposed method can be flexibly applied to any type of palmprint feature fusion. Moreover, it presents the multiview features in a low-dimensionality sub-space, effectively reducing the computational complexity. Experimental results on various palmprint databases have shown that the proposed method can always achieve the best recognition performance compared to other state-of-the-art algorithms.