Abstract
We introduce in this paper two probabilistic reasoning models (PRM-1 and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognition problem. The conditional probability density function for each class is modeled using the within class scatter and the Maximum A Posteriori (MAP) classification rule is implemented in the reduced PCA subspace. Experiments carried out using 1107 facial images corresponding to 369 subjects (with 169 subjects having duplicate images) from the FERET database show that the PRM approach compares favorably against the two well-known methods for face recognition - the Eigenfaces and Fisherfaces.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 827-832 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| State | Published - 1998 |
| Externally published | Yes |
| Event | Proceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA Duration: Jun 23 1998 → Jun 25 1998 |
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition