An evidential reasoning based classification algorithm and its application for face recognition with class noise

Xiaodong Wang, F. Liu, L. C. Jiao, Zhiguo Zhou, Jingjing Yu, Bing Li, Jianrui Chen, Jiao Wu, Fanhua Shang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

For classification problems, in practice, real-world data may suffer from two types of noise, attribute noise and class noise. It is the key for improving recognition performance to remove as much of their adverse effects as possible. In this paper, a formalism algorithm is proposed for classification problems with class noise, which is more challenging than those with attribute noise. The proposed formalism algorithm is based on evidential reasoning theory which is a powerful tool to deal with uncertain information in multiple attribute decision analysis and many other areas. Thus, it may be more effective alternative to handle noisy label information. And then a specific algorithm - Evidential Reasoning based Classification algorithm (ERC) is derived to recognize human faces under class noise conditions. The proposed ERC algorithm is extensively evaluated on five publicly available face databases with class noise and yields good performance.

Original languageEnglish (US)
Pages (from-to)4117-4128
Number of pages12
JournalPattern Recognition
Volume45
Issue number12
DOIs
StatePublished - Dec 2012

Keywords

  • Class noise
  • Evidential reasoning
  • Face recognition
  • Linear regression classification (LRC)
  • Sparse representation-based classification (SRC)

ASJC Scopus subject areas

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

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