Multifactor feature extraction for human movement recognition

Bo Peng, Gang Qian, Yunqian Ma, Baoxin Li

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In this paper, we systematically examine multifactor approaches to human pose feature extraction and compare their performances in movement recognition. Two multifactor approaches have been used in pose feature extraction, including a deterministic multilinear approach and a probabilistic approach based on multifactor Gaussian process. These two approaches are compared in terms of the degrees of view-invariance, reconstruction capacity, performances in human pose and gesture recognition using real movement datasets. The experimental results show that the deterministic multilinear approach outperforms the probabilistic-based approach in movement recognition.

Original languageEnglish (US)
Pages (from-to)375-389
Number of pages15
JournalComputer Vision and Image Understanding
Issue number3
StatePublished - Mar 2011


  • Feature extraction
  • Gesture recognition
  • Multifactor analysis
  • Pose recognition
  • View invariance

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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