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by Xiaoming Liu, Tsuhan Chen, B. V. K. Vijaya Kumar
Proc. 2002 Int. Conf. Automatic Face, Gesture Recognition
http://amp.ece.cmu.edu/Publication/Xiaoming/FG2002_xiaoming1.pdf
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Abstract:
In this paper, we present a scheme for face authentication by using applying principal component analysis (PCA) to model variations. To deal with variations, such as facial expressions and registration errors, with which traditional appearancebased methods do not perform well, we propose the eigenflow approach. In this approach, the optical flow and the optical flow residue between a test image and a well-registered image in the training set are first computed. The optical flow is then fitted to a model that is pre-trained by applying PCA to optical flows resulting from facial expressions and registration errors for the subjects. The eigenflow residue, optimally combined with the optical flow residue using linear discriminant analysis (LDA), determines the authenticity of the test image. Experimental results show that the proposed scheme outperforms the traditional methods in the presence of facial variations. 1.
Citations
|
1745
|
Eigenfaces for Recognition
– Turk, Pentland
- 1991
|
|
964
|
An iterative image registration technique with an application to stereo vision
– Lucas, Kanade
- 1981
|
|
739
|
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” Proc
– Belhumeur, Hespanha, et al.
- 1997
|
|
478
|
View-based and modular eigenspaces for face recognition
– Pentland, Moghaddam, et al.
- 1994
|
|
402
|
Human and Machine Recognition of faces: A survey
– Chellappa, Sirohey
- 1995
|
|
399
|
Recognition: Features versus Template
– Brunelli, Poggio
- 1993
|
|
356
|
The FERET Evaluation Methodology for Face-Recognition Algorithms
– Phillips, Moon, et al.
- 2000
|
|
101
|
Rate-distortion methods for image and video compression
– Ortega, Ramchandran
- 1998
|
|
99
|
Recognizing human facial expressions from long image sequences using optical flow
– Yacoob, Davis
- 1996
|
|
90
|
Velocity determination in scenes containing several moving objects
– Fennema, Thompson
- 1979
|
|
67
|
D.G.: Pattern Classification. Second Edition
– Duda, Hart, et al.
- 2000
|
|
59
|
V.: E cient calculation of primary images from a set of images
– Murakami, Kumar
- 1982
|
|
53
|
Subspace linear discriminant analysis for face recognition
– Zhao, Chellappa, et al.
- 1999
|
|
47
|
Picture processing system by computer complex and recognition of human faces
– Kanade
- 1973
|
|
21
|
A survey of face recognition
– Fromherz, Stucki, et al.
- 1997
|
|
8
|
Automated Facial Expression Recognition
– Lien, Zlochower, et al.
- 1998
|
|
7
|
Estimation of classification error
– Fukunaga, Kessell
- 1971
|
|
5
|
Optical flow applied to person identification
– Kruizinga, Petkov
- 1994
|
|
3
|
Frontal Face Authentication Using Discriminating Grids with Morphological Feature Vectors
– Kotropoulos, Tefas, et al.
- 2000
|
|
3
|
Bayesian face Recognition. Pattern Recognition
– Moghaddam, Jebara, et al.
- 2000
|
|
3
|
Statistical Pattern Rcognition
– Fukunaga
- 1989
|
