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Human Face Identification from Video Based on Frequency Domain Asymmetry Representation Using Hidden Markov Models

  • Sinjini Mitra
  • Marios Savvides
  • B. V. K. Vijaya Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

Abstract

In this paper we introduce a novel human face identification scheme from video data based on a frequency domain representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.

Keywords

Hide Markov Model Video Sequence Gesture Recognition Facial Asymmetry Automatic Face 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sinjini Mitra
    • 1
  • Marios Savvides
    • 2
  • B. V. K. Vijaya Kumar
    • 2
  1. 1.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA
  2. 2.Electrical and Computer Engineering DepartmentCarnegie Mellon UniversityPittsburghUSA

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