Active AU based patch weighting for facial expression recognition

Weicheng Xie, Linlin Shen, Meng Yang, Zhihui Lai

Research output: Journal PublicationArticlepeer-review

13 Citations (Scopus)

Abstract

Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+) databases, respectively. Better cross-database performance has also been observed.

Original languageEnglish
Article number275
JournalSensors
Volume17
Issue number2
DOIs
Publication statusPublished - 1 Feb 2017
Externally publishedYes

Keywords

  • Active AU detection
  • AU weighting
  • Expression recognition
  • Expression triplet
  • Feature optimization

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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