Palmprint recognition based on the line feature local tri-directional patterns

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.