Facial Expression Recognition via Regression-Based Robust Locality Preserving Projections

Jingjie YAN  Bojie YAN  Ruiyu LIANG  Guanming LU  Haibo LI  Shipeng XIE  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.2   pp.564-567
Publication Date: 2018/02/01
Publicized: 2017/11/06
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8202
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
facial expression recognition,  regression-based robust locality preserving projections (RRLPP),  augmented Lagrangian multiplier,  

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In this paper, we present a novel regression-based robust locality preserving projections (RRLPP) method to effectively deal with the issue of noise and occlusion in facial expression recognition. Similar to robust principal component analysis (RPCA) and robust regression (RR) approach, the basic idea of the presented RRLPP approach is also to lead in the low-rank term and the sparse term of facial expression image sample matrix to simultaneously overcome the shortcoming of the locality preserving projections (LPP) method and enhance the robustness of facial expression recognition. However, RRLPP is a nonlinear robust subspace method which can effectively describe the local structure of facial expression images. The test results on the Multi-PIE facial expression database indicate that the RRLPP method can effectively eliminate the noise and the occlusion problem of facial expression images, and it also can achieve better or comparative facial expression recognition rate compared to the non-robust and robust subspace methods meantime.