High-performance face recognition papers

[Simon Lucas]

The following papers are available on-line from
      http://esewww.essex.ac.uk/~sml 
 describing the continuous n-tuple classifer and
 its application to face recognition.  


 The method offers high speed 
 (can match an unknown image with about 4,000 'face-models'
 per second on a PC) and high accuracy. 
 The BMVC '97 paper gives a more complete 
 description of the system, while the Electronics Letters
 paper provides a more significant set of results.

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     The continuous n-tuple classifier and 
     its application to face recognition

     S.M. Lucas

     Electronics Letters, v33, pp 1676 - 1678

 Abstract:  This paper describes the continuous n-tuple classifier:
 a new type of n-tuple classifier that is well suited to 
 problems where the input is continuous or multi-level rather
 than binary. Results on a widely used face database show
 the continuous n-tuple classifier to be as accurate as any method 
reported in the literature, while having the advantages of speed and 
simplicity over other methods. 


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     Face Recognition with the continuous n-tuple classifier 

     S.M. Lucas

     In proceedings of Britich Machine Vision Conference-97 

 Abstract:  Face recognition is an important field of research with
 many potential applications for suitably efficient 
 systems, including biometric security and searching large face 
 databases. This paper describes a new 
 approach to the problem based on a new type of 
 n-tuple classifier: the continuous n-tuple system. 
 Results indicate that the new method is 
 faster and more accurate than previous methods reported in the   
literature on the widely used Olivetti Research Laboratories face   
database. 

  Comments welcome.

  Simon Lucas 

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Dr. Simon Lucas
Department of Electronic Systems Engineering
University of Essex
Colchester CO4 3SQ
United Kingdom

Tel:    (+44) 1206 872935
Fax:    (+44) 1206 872900
Email:  sml at essex.ac.uk
http://esewww.essex.ac.uk/~sml
secretary:  Mrs Wendy Ryder (+44) 1206 872437
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