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A new framework for small sample size face recognition based on weighted multiple decision templates

Ghaemi, M. S ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-17537-4_58
  3. Publisher: 2010
  4. Abstract:
  5. In this paper a holistic method and a local method based on decision template ensemble are investigated. In addition by combining both methods, a new hybrid method for boosting the performance of the system is proposed and evaluated with respect to robustness against small sample size problem in face recognition. Inadequate and substantial variations in the available training samples are the two challenging obstacles in classification of an unknown face image. At first in this novel multi learner framework, a decision template is designed for the global face and a set of decision templates is constructed for each local part of the face as a complement to the previous part. The prominent results demonstrate that, the new hybrid method based on fusion of weighted multiple decision templates is superior to the other classic combining schemes for both ORL and Yale data sets. In addition when the global and the local components of the face are combined together the best performance is achieved
  6. Keywords:
  7. Combining schemes ; Data sets ; Decision template ; Face images ; Hybrid method ; Local methods ; Local parts ; Small Sample Size ; Small sample size problems ; Substantial variations ; Training sample ; Data processing ; Face recognition
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 22 November 2010 through 25 November 2010, Sydney, NSW ; Volume 6443 LNCS, Issue PART 1 , November , 2010 , Pages 470-477 ; 03029743 (ISSN) ; 3642175368 (ISBN)
  9. URL: http://link.springer.com/chapter/10.1007%2F978-3-642-17537-4_58