Fast and robust face recognition via coding residual map learning based adaptive masking

Meng Yang, Zhizhao Feng, Chi Keung Simon Shiu, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

32 Citations (Scopus)


Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. Recently, the representation (or coding) based FR schemes with sparse coding coefficients and coding residual have demonstrated good robustness to face occlusion; however, the high complexity of l1-minimization makes them less useful in practical applications. In this paper we propose a novel coding residual map learning scheme for fast and robust FR based on the fact that occluded pixels usually have higher coding residuals when representing an occluded face image over the non-occluded training samples. A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. Finally the face image is identified by masking the detected occlusion pixels from face representation. Experiments on benchmark databases show that the proposed scheme has much lower time complexity but comparable FR accuracy with other popular approaches.
Original languageEnglish
Pages (from-to)535-543
Number of pages9
JournalPattern Recognition
Issue number2
Publication statusPublished - 1 Feb 2014


  • Adaptive masking
  • Coding residual map
  • Robust face recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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