Most research on face recognition has focused on representation of face appearances rather than the classifiers. For robust classification performance, we need to adopt elaborate classifiers. Output coding is suitable for this purpose because it can allow online learning. In this paper, we propose an N-division output coding method. In the experiments we demonstrate such properties as problem complexity, margin of separation, machine relevance and the recognition performance among different output coding methods.
|Number of pages||9|
|Journal||Pattern Recognition Letters|
|Publication status||Published - 2003 Dec|
Bibliographical noteFunding Information:
This work was supported (in part) by Korea Science and Engineering Foundation (KOSEF) through Biometrics Engineering Research Center (BERC) at Yonsei University.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence