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Contactless palmprint recognition attracted much attention in recent years for it is more user-friendly and sanitary compared with contact palmprint recognition. However, due to the lack of restrictions on the position of the palms when collecting images, there are severe translation and rotation in contactless palmprint images, which will seriously affect the recognition accuracy. Conventional palmprint recognition methods based on the hand-craft features mainly focus on the characteristics of palmprint images, but the correlations among samples are usually neglected. Therefore, it is urgent that extracting the stable and discriminative features to improve the recognition performance. To solve this problem, a joint multi half-orientation features learning method (JMHOFL) was proposed in this article. First, we extracted the orientation features using banks of half-Gabor filters, and constructed the multi half-orientation features (MHOF) of the palmprint image. To overcome the effects of translation and rotation, MHOF obtained multi orientation codes and performed block-wise statistics on these orientation codes. Afterwards, a joint low-rank inter-class sparsity least squares regression (JLRICS_LSR) was proposed to study more stable and discriminative features from MHOF. JLRICS_LSR takes into account the structure between samples, and reduces the influence of noises. Lastly, Euclidean distance is used for feature matching. Experiments on CASIA, IITD, and Tongji palmprint databases showed the promising performance of the proposed method.
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