Robust local features for remote face recognition

Jie Chen, Li Liu, Vili Kellokumpu, Guoying Zhao, Matti Pietikäinen, Vishal M. Patel, Rama Chellappa

Research output: Contribution to journalArticle

12 Citations (Scopus)


In this paper, we propose a robust local descriptor for face recognition. It consists of two components, one based on a shearlet-decomposition and the other on local binary pattern (LBP). Shearlets can completely analyze the singular structures of piecewise smooth images, which is useful since singularities and irregular structures carry useful information in an underlying image. Furthermore, LBP is effective for describing the edges extracted by shearlets even when the images contain high level of noise. Experimental results using the Face Recognition Grand Challenge dataset show that the proposed local descriptor significantly outperforms many widely used features (e.g., Gabor and deep learning-based features) when the images are corrupted by random noise, demonstrating robustness to noise. In addition, experimental results show promising results for two challenging datasets which have poor image quality, i.e., a remote face dataset and the Point and Shoot Face Recognition Challenge dataset.

Original languageEnglish
Pages (from-to)34-46
Number of pages13
JournalImage and Vision Computing
Publication statusPublished - Aug 2017
MoE publication typeNot Eligible



  • Remote face recognition
  • Robust local feature

Cite this

Chen, J., Liu, L., Kellokumpu, V., Zhao, G., Pietikäinen, M., Patel, V. M., & Chellappa, R. (2017). Robust local features for remote face recognition. Image and Vision Computing, 64, 34-46.