Robust and Low-Rank Representation for Fast Face Identification With Occlusions

• Published in: IEEE transactions on image processing Vol. 26; no. 5; pp. 2203 - 2218
• Main Authors: Iliadis, Michael, Haohong Wang, Molina, Rafael, Katsaggelos, Aggelos K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Estimation; Face; Face identification; Iterative methods; iterative reweighted coding; Lighting; low-rank estimation; robust representation; Robustness; Training; Training data
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2017.2675206

In this paper, we propose an iterative method to address the face identification problem with block occlusions. Our approach utilizes a robust representation based on two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. The first fits to the errors a distribution described by a tailored loss function. The second describes the error image as having a specific structure (resulting in low-rank in comparison with image size). We will show that this joint characterization is effective for describing errors with spatial continuity. Our approach is computationally efficient due to the utilization of the alternating direction method of multipliers. A special case of our fast iterative algorithm leads to the robust representation method, which is normally used to handle non-contiguous errors (e.g., pixel corruption). Extensive results on representative face databases (in constrained and unconstrained environments) document the effectiveness of our method over existing robust representation methods with respect to both identification rates and computational time.