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Face recognition across large pose variations via boosted tied factor analysis

Khaleghian, S ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1109/WACV.2011.5711502
  3. Publisher: 2011
  4. Abstract:
  5. In this paper, we propose an ensemble-based approach to boost performance of Tied Factor Analysis(TFA) to overcome some of the challenges in face recognition across large pose variations. We use Adaboost.m1 to boost TFA which has shown to possess state-of-the-art face recognition performance under large pose variations. To this end, we have employed boosting as a discriminative training in the TFA as a generative model. In this model, TFA is used as a base classiœr for the boosting algorithm and a weighted likelihood model for TFA is proposed to adjust the importance of each training data. Moreover, a modiÔd weighting and a diversity criterion are used to generate more diverse classiœrs in the boosting process. Experimental results on the FERET data set demonstrated the improved performance of the Boosted Tied Factor Analysis(BTFA) in comparison with TFA for lower dimensions when a holistic approach is being used
  6. Keywords:
  7. Boosting algorithm ; Data sets ; Discriminative training ; Face recognition performance ; Factor analysis ; Generative model ; Holistic approach ; Pose variation ; Training data ; Computer applications ; Computer vision ; Face recognition
  8. Source: 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, 5 January 2011 through 7 January 2011 ; January , 2011 , Pages 190-195 ; 9781424494965 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5711502