Face recognition using view-based and modular eigenspaces

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.

[1]  David Casasent,et al.  Principal-Component Imagery For Statistical Pattern Recognition Correlators , 1982 .

[2]  Ernest L. Hall,et al.  Intelligent Robots and Computer Vision VI , 1987 .

[3]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Alex Pentland,et al.  Face Processing: Models For Recognition , 1990, Other Conferences.

[5]  Yehezkel Yeshurun,et al.  Detection of interest points using symmetry , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[6]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[7]  W. Welsh,et al.  Facial-feature image coding using principal components , 1992 .

[8]  D. J. Myers,et al.  Automatic location of visual features by a system of multilayered perceptrons , 1992 .

[9]  Alex Pentland,et al.  Space-time gestures , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Hiroshi Murase,et al.  Learning and recognition of 3D objects from appearance , 1993, [1993] Proceedings IEEE Workshop on Qualitative Vision.

[11]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Electronic Imaging.

[12]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.