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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ming-Hsuan Yang Abuja, N. Kriegman, D. |
| Copyright Year | 2000 |
| Description | Author affiliation: Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA (Ming-Hsuan Yang) |
| Abstract | We present two methods using mixtures of linear sub-spaces for face detection in gray level images. One method uses a mixture of factor analyzers to concurrently perform clustering and, within each cluster, perform local dimensionality reduction. The parameters of the mixture model are estimated using an EM algorithm. A face is detected if the probability of an input sample is above a predefined threshold. The other mixture of subspaces method uses Kohonen's self-organizing map for clustering and Fisher linear discriminant to find the optimal projection for pattern classification, and a Gaussian distribution to model the class-conditioned density function of the projected samples for each class. The parameters of the class-conditioned density functions are maximum likelihood estimates and the decision rule is also based on maximum likelihood. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions are used as the training set to capture the variations of human faces. Our methods have been tested on three sets of 225 images which contain 871 faces. Experimental results on the first two datasets show that our methods perform as well as the best methods in the literature, yet have fewer false detects. |
| Starting Page | 70 |
| Ending Page | 76 |
| File Size | 155071 |
| Page Count | 7 |
| File Format | |
| ISBN | 0769505805 |
| DOI | 10.1109/AFGR.2000.840614 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-03-30 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face detection Density functional theory Maximum likelihood detection Maximum likelihood estimation Performance analysis Clustering algorithms Pattern classification Gaussian distribution Humans Testing |
| Content Type | Text |
| Resource Type | Article |
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