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Face Recognition: A Critical Look at Biologically-Inspired Approaches
G. Sukthankar
tech. report CMU-RI-TR-00-04, Robotics Institute, Carnegie Mellon University, January, 2000.

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Abstract

This paper analyzes the merits of two biologically-inspired face recognition models, eigenfaces and graph-matching, in the context of related neurophysiological and psychophysical data. Given the ambiguity of current biological evidence, a more promising direction for future face recognition research is in the development of models that conform more closely to human perception of facial similarity.

Text Reference

G. Sukthankar, Face Recognition: A Critical Look at Biologically-Inspired Approaches, tech. report CMU-RI-TR-00-04, Robotics Institute, Carnegie Mellon University, January, 2000.

BibTeX Reference

@techreport{Sukthankar_2000_3275,
   author = "Gita Sukthankar",
   title = "Face Recognition: A Critical Look at Biologically-Inspired Approaches",
   institution = "Robotics Institute, Carnegie Mellon University",
   month = "January",
   year = "2000",
   number = "CMU-RI-TR-00-04",
   address = "Pittsburgh, PA"
}