Name
Abstract | ||
|---|---|---|
The fixed mean shape that is built from the statistical shape model produces an erroneous feature extraction result when ASM is applied to multipose faces. To remedy this problem the mean shape vector which is similar to an input face image is needed. In this paper, we propose the adaptive mean shape to extract facial features accurately for non frontal face. It indicates the mean shape vector that is the most similar to the face form of the input image. Our experimental results show that the proposed method obtains feature point positions with high accuracy and significantly improving the performance of facial feature extraction over and above that of the original ASM. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-71457-6_38 | MIRAGE |
Keywords | Field | DocType |
adaptive mean shape,statistical shape model,mean shape vector,facial feature extraction,input face image,erroneous feature extraction result,fixed mean shape,active shape model,facial feature point extraction,non frontal face,face form,facial feature,feature extraction | Computer vision,Active shape model,Point distribution model,Landmark point,Pattern recognition,Computer science,Gesture recognition,Feature extraction,Artificial intelligence | Conference |
Volume | ISSN | Citations |
4418 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun-Chul Kim | 1 | 0 | 0.68 |
| Hyoung-Joon Kim | 2 | 9 | 2.02 |
| Wonjun Hwang | 3 | 174 | 18.82 |
| Seok-cheol Kee | 4 | 129 | 13.94 |
| Whoi-Yul Kim | 5 | 518 | 47.84 |