Abstract
The inaugural ACM Multimedia Conference coincided with a surge of interest in computer vision technologies for detecting and recognizing people and their activities in images and video. Face recognition was the first of these topics to broadly engage the vision and multimedia research communities. The Eigenfaces approach was, deservedly or not, the method that captured much of the initial attention, and it continues to be taught and used as a benchmark over 20 years later. This article is a brief personal view of the genesis of Eigenfaces for face recognition and its relevance to the multimedia community.
- Burt, P. J. 1988. Smart sensing within a pyramid vision machine. Proc. IEEE 76, 8, 1006--1015.Google Scholar
Cross Ref
- Fleming, M. and Cottrell, G. 1990. Categorization of faces using unsupervised feature extraction. In Proceedings of the International Joint Conference on Neural Networks. Vol. 2, 65--70.Google Scholar
- Fukunaga, K. 1990. Introduction to Statistical Pattern Recognition. Academic Press. Google Scholar
Digital Library
- Harmon, L. D., Khan, M. K., Lasch, R., and Ramig, P. F. 1981. Machine identification of human faces. Pattern Recognition 13, 2, 97--110.Google Scholar
Cross Ref
- Kanade, T. 1973. Picture processing system by computer complex and recognition of human faces. Ph.D. dissertation, Department of Information Science, Kyoto University.Google Scholar
- Kelly, M. D. 1970. Visual identification of people by computer. Stanford Artificial Intelligence Project Memo AI-130.Google Scholar
- Kuhn, R., Nguyen, P., Junqua, J.-C., Goldwasser, L., Niedzielski, N., Fincke, S., Field, K., and Contolini, M. 1998. Eigenvoices for speaker adaptation. In Proceedings of the International Conference on Spoken Language Processing.Google Scholar
- Midorikawa, H. 1988. The face pattern identification by back-propagation learning procedure. In Abstracts of the 1st Annual INNS Meeting. 515.Google Scholar
Cross Ref
- Moghaddam, B., Wahid, W., and Pentland, A. 1998. Beyond eigenfaces: Probabilistic matching for face recognition. In Proceedings of the 3rd International Conference on Automatic Face- and Gesture-Recognition. 30--35. Google Scholar
Digital Library
- Pentland, A., Moghaddam, B., and Starner, T. 1994. View-based and modular eigenspaces for face recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition. 84--91.Google Scholar
- Sirovich, L. and Kirby, M. 1987. Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A 4, 3, 519--524.Google Scholar
Cross Ref
- Turk, M. 1991. Interactive-time vision: Face recognition as a visual behavior. Ph.D. dissertation, MIT Media Lab, Cambridge, MA. Google Scholar
Digital Library
- Wong, K., Law, H., and Tsang, P. 1989. A system for recognising human faces. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1638--1642.Google Scholar
- Yuille, A. L., Cohen, D. S., and Hallinan, P. W. 1989. Feature extraction from faces using deformable templates. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
Index Terms
Over twenty years of eigenfaces
Recommendations
Face recognition using the mixture-of-eigenfaces method
This paper deals with face recognition using the mixture-of-eigenfaces method. The well-known eigenface method uses one set of holistic facial features obtained by principal component analysis (PCA). However, a single set of eigenfaces is not enough to ...
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We ...
Age-Invariant face recognition using shape transformation
ICIC'13: Proceedings of the 9th international conference on Intelligent Computing TheoriesThis paper proposes a novel facial image transformation that minimizes the variation due to aging in facial features. It transforms a face image of an individual according to the probe image to register the facial features. The images are globally ...






Comments