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Proceedings Paper

Face recognition using view-based and modular eigenspaces
Author(s): Baback Moghaddam; Alexander P. Pentland
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Paper Abstract

In this paper we describe experiments using eigenfaces for recognition and interactive search in the FERET face database. A recognition accuracy of 99.35% is obtained using frontal views of 155 individuals. This figure is consistent with the 95% recognition rate obtained previously on a much larger database of 7,562 `mugshots' of approximately 3,000 individuals, consisting of a mix of all age and ethnic groups. We also demonstrate that we can automatically determine head pose without significantly lowering recognition accuracy; this is accomplished by use of a view-based multiple-observer eigenspace technique. In addition, a modular eigenspace description is used which incorporates salient facial features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields slightly higher recognition rates as well as a more robust framework for face recognition. In addition, a robust and automatic feature detection technique using eigentemplates is demonstrated.

Paper Details

Date Published: 25 October 1994
PDF: 10 pages
Proc. SPIE 2277, Automatic Systems for the Identification and Inspection of Humans, (25 October 1994); doi: 10.1117/12.191877
Show Author Affiliations
Baback Moghaddam, Massachusetts Institute of Technology (United States)
Alexander P. Pentland, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2277:
Automatic Systems for the Identification and Inspection of Humans
Richard J. Mammone; J. David Murley, Editor(s)

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