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

Michael Iliadis, Haohong Wang, Rafael Molina, Aggelos K. Katsaggelos

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number7864430
Pages (from-to)2203-2218
Number of pages16
JournalIEEE Transactions on Image Processing
Volume26
Issue number5
DOIs
StatePublished - May 2017

Keywords

  • Face identification
  • Iterative reweighted coding
  • Low-rank estimation
  • Robust representation

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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