Paper
25 October 1994 Face recognition using view-based and modular eigenspaces
Baback Moghaddam, Alexander P. Pentland
Author Affiliations +
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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Baback Moghaddam and Alexander P. Pentland "Face recognition using view-based and modular eigenspaces", Proc. SPIE 2277, Automatic Systems for the Identification and Inspection of Humans, (25 October 1994); https://doi.org/10.1117/12.191877
Lens.org Logo
CITATIONS
Cited by 190 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Databases

Facial recognition systems

Head

Mouth

Nose

Sensors

Eye

RELATED CONTENT


Back to Top