Low-rank constrained collaborative representation for robust face recognition Conference Paper uri icon

abstract

  • 2017 IEEE. Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this paper, we present a novel robust face recognition algorithm based on class-wise low-rank constrained collaborative representations. We impose a low-rank constraint on the representation coefficient matrix to discriminate against outliers. The resulting low-rank constrained collaborative representation based classifier (LCRC) jointly minimizes the class-wise reconstruction error and rank of coefficient matrix. Experiments show that LCRC outperforms popular classifiers such as SRC, CRC, SVM, PROCRC on the AR, CMU PIE and LFW databases.

name of conference

  • 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)

published proceedings

  • 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)

author list (cited authors)

  • Lu, T., Guan, Y., Chen, D., Xiong, Z., & He, W.

citation count

  • 5

complete list of authors

  • Lu, Tao||Guan, Yingjie||Chen, Deng||Xiong, Zixiang||He, Wei

publication date

  • October 2017

publisher