Abstract
A face detection system that automatically locates faces in gray-level images is described. Also described is a system which matches a given face image with faces in a database. Face detection in an image is performed by template matching using templates derived from a selected set of normalized faces. Instead of using original gray level images, vertical gradient images were calculated and used to make the system more robust against variations in lighting conditions and skin color. Faces of different sizes are detected by processing the image at several scales. Further, a coarse- to-fine strategy is used to speed up the processing, and a combination of whole face and face component templates are used to ensure low false detection rates. The input to the face recognition system is a normalized vertical gradient image of a face, which is compared against a database using a set of pretrained feedforward neural networks with a winner-take-all fuser. The training is performed by using an adaptation of the backpropagation algorithm. This system has been developed and tested using images from the FERET database and a set of images obtained from Rowley, et al and Sung and Poggio.
| Original language | English |
|---|---|
| Pages (from-to) | 90-99 |
| Number of pages | 10 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 2940 |
| DOIs | |
| State | Published - 1997 |
| Event | National and International Law Enforcement Databases - Boston, MA, United States Duration: Nov 19 1996 → Nov 19 1996 |
Keywords
- Correlation coefficient
- Face detection
- Face recognition
- Neural networks
- Template matching