Data uncertainty in face recognition

Yong Xu, Xiaozhao Fang, Xuelong Li, Jiang Yang, Jia You, Hong Liu, Shaohua Teng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

131 Citations (Scopus)

Abstract

The image of a face varies with the illumination, pose, and facial expression, thus we say that a single face image is of high uncertainty for representing the face. In this sense, a face image is just an observation and it should not be considered as the absolutely accurate representation of the face. As more face images from the same person provide more observations of the face, more face images may be useful for reducing the uncertainty of the representation of the face and improving the accuracy of face recognition. However, in a real world face recognition system, a subject usually has only a limited number of available face images and thus there is high uncertainty. In this paper, we attempt to improve the face recognition accuracy by reducing the uncertainty. First, we reduce the uncertainty of the face representation by synthesizing the virtual training samples. Then, we select useful training samples that are similar to the test sample from the set of all the original and synthesized virtual training samples. Moreover, we state a theorem that determines the upper bound of the number of useful training samples. Finally, we devise a representation approach based on the selected useful training samples to perform face recognition. Experimental results on five widely used face databases demonstrate that our proposed approach can not only obtain a high face recognition accuracy, but also has a lower computational complexity than the other state-of-the-art approaches.
Original languageEnglish
Article number6729058
Pages (from-to)1950-1961
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume44
Issue number10
DOIs
Publication statusPublished - 1 Oct 2014

Keywords

  • Computer vision
  • face recognition
  • machine learning
  • pattern recognition
  • uncertainty

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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