Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database.
|Number of pages||5|
|Journal||Artificial Life and Robotics|
|Publication status||Published - Dec 1 2008|
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Biochemistry, Genetics and Molecular Biology(all)