Sparse Representation for Video-Based Face Recognition

  • Imran Naseem
  • Roberto Togneri
  • Mohammed Bennamoun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


In this paper we address for the first time, the problem of video-based face recognition in the context of sparse representation classification (SRC). The SRC classification using still face images, has recently emerged as a new paradigm in the research of view-based face recognition. In this research we extend the SRC algorithm for the problem of temporal face recognition. Extensive identification and verification experiments were conducted using the VidTIMIT database [1,2]. Comparative analysis with state-of-the-art Scale Invariant Feature Transform (SIFT) based recognition was also performed. The SRC algorithm achieved 94.45% recognition accuracy which was found comparable to 93.83% results for the SIFT based approach. Verification experiments yielded 1.30% Equal Error Rate (EER) for the SRC which outperformed the SIFT approach by a margin of 0.5%. Finally the two classifiers were fused using the weighted sum rule. The fusion results consistently outperformed the individual experts for identification, verification and rank-profile evaluation protocols.


Face Recognition Video Sequence Sparse Representation Independent Component Analysis Scale Invariant Feature Transform 
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 2009

Authors and Affiliations

  • Imran Naseem
    • 1
  • Roberto Togneri
    • 1
  • Mohammed Bennamoun
    • 2
  1. 1.School of Electrical, Electronic and Computer EngineeringThe University of Western AustraliaAustralia
  2. 2.School of Computer Science and Software EngineeringThe University of WesternAustralia

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