The Robotics Institute

Carnegie Mellon Robotics Institute

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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.

Keywords

vision, face recognition, biological modeling

Notes

Text Reference

Gita 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",
   booktitle = "",
   institution = "Robotics Institute",
   month = "January",
   year = "2000",
   number= "CMU-RI-TR-00-04",
   address= "Pittsburgh, PA",
}

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