Multi-order Texture Features for Palmprint Recognition

This repository is a Matlab implementation of our 2TCC and MTCC (accepted by Artificial Intelligence Review)

Paper Link

Abstract

Palmprint attracts increasing attention thanks to its several advantages. 1st-order textures have been widely used for palmprint recognition; unfortunately, high-order textures, although they are also discriminative, were ignored in the existing works. 2nd-order textures are first employed for palmprint recognition in this paper. 1st-order textures are convolved with the filters to extract 2nd-order textures that can refine the texture information and improve the contrast of the feature map. Then 2nd-order textures are used to generate 2nd-order Texture Co-occurrence Code (2TCC). The sufficient experiments demonstrate that 2TCC yields satisfactory accuracy performance on four public databases, including contact, contactless and multi-spectral acquisition types. Moreover, in order to further improve the discrimination and robustness of 2TCC, we propose Multiple-order Texture Co-occurrence Code (MTCC), in which 1st-order Texture Co-occurrence Code (1TCC) and 2TCC are fused at score level. 1TCC is good at describing minor wrinkles; while 2TCC does well in describing principal textures. Thus the combination of both can describe the palmprint features more comprehensively. MTCC achieves remarkable accuracy performance when compared with the state-of-the-art methods on all public databases.

Some tips

Our codes can be easily modifed to other coding-based methods, such as PalmCode, BOCV, CompCode, etc.

Acknowledgments

Thanks to my all cooperators, especially TF Wu and Lu Leng. They contributed so much to this work.

Citation

If our work is valuable to you, please cite our work:

@article{yang2022mtcc,
  title={Multi-order Texture Features for Palmprint Recognition},
  author={Yang, Ziyuan and Leng, Lu and Wu, Tengfei and Li, Ming and Chu, Jun},
  journal={Artificial Intelligence Review},
  year={2022},
  publisher={Springer}
}