Recent researches have shown that the texture descriptor local tri‐directional patterns (LTriDP) performs well in many recognition tasks. However, LTriDP cannot effectively describe the structure of palm lines, which results in poor palmprint recognition. To overcome this issue, this work proposes a modified version of LTriDP, called line feature local tri‐directional patterns (LFLTriDP), which takes into account the texture features of the palmprint. First, since palmprints contain rich lines, the line features of palmprint images, including orientation and magnitude, are extracted. The line features are more robust to variations compared to the original grayscale values. Then, the directional features are encoded as tri‐directional patterns. The tri‐directional patterns reflect the direction changes in the local area. Finally, the LFLTriDP features are constructed by the tri‐directional patterns, orientation and magnitude features. The LFLTriDP features effectively describe the structure of palm lines. Considering that most palm lines are curved, we use the concavity as supplementary information. The concavity of each pixel is obtained using the Banana filter and all pixels are grouped into two categories. The LFLTriDP features are refined to generate two feature vectors by the concavity to enhance the discriminability. The matching scores of the two feature vectors are weighted differently in the matching stage to reduce intra‐class distance and increase inter‐class distance. Experiments on PolyU, PolyU Multi‐spectral, Tongji, CASIA and IITD palmprint databases show that LFLTriDP achieves promising recognition performance.
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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