As a relatively new biometric trait, Finger-Knuckle-Print (FKP) plays a vital role in establishing a personal authentication system in modern society due to its rich discriminative features, low time cost in image capture and user-friendliness. However, most existing KFP descriptors are hand-crafted and fail to work well with limited training samples. In this paper, we propose a feature learning method for few-shot FKP recognition by jointly learning compact multi-view hash codes (JLCMHC) of a FKP image. We first form the multi-view data vectors (MVDV) to exploit the multiple feature-specific information from a FKP image. Then, we learn a feature projection to encode the MVDV into compact binary codes in an unsupervised manner, where 1) the variance of the learned feature codes on each view is maximized and 2) the difference of the inter-view binary codes is enlarged, so that the redundant information in MVDV is reduced and more informative features can be obtained. Lastly, we pool the binary codes into block-wise statistics features as the final descriptor for FKP representation and recognition. Experimental results on the existing benchmark FKP databases clearly show that the JLCMHC method outperforms the state-of-the-art FKP descriptors.