Name
Abstract | ||
|---|---|---|
We propose an efficient and generic facial feature localization method based on a weighted vector concentration approach. Our method does not require any specific priors on facial shape but implicitly learns its structural information from a training data. Unlike previous work, facial feature points are globally estimated by the concentration of directional vectors from sampling points on a face region, and those vectors are weighted by using local likelihood patterns which discriminate the appropriate position of the feature points. The directional vectors and local likelihood patterns are provided through nearest neighbor search between local patterns around the sampling points and a trained codebook of extended templates. The combination of the global vector concentration and the verification with the local likelihood patterns achieves robust facial feature point detection. We demonstrate that our method outperforms state-of-the-art method based on the Active Shape Models in our evaluation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.imavis.2009.09.008 | Image Vision Comput. |
Keywords | Field | DocType |
feature point,local pattern,local likelihood pattern,generic facial feature localization,facial feature localization,facial shape,state-of-the-art method,weighted vector concentration approach,robust facial feature point,facial feature point,directional vector,sampling point,weighted vector concentration,nearest neighbor search,active shape model | Training set,Computer vision,Pattern recognition,Sampling (statistics),Artificial intelligence,Template,Prior probability,Mathematics,Nearest neighbor search,Codebook | Journal |
Volume | Issue | ISSN |
28 | 5 | Image and Vision Computing |
Citations | PageRank | References |
19 | 0.83 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatsuo Kozakaya | 1 | 89 | 6.68 |
| Tomoyuki Shibata | 2 | 58 | 4.77 |
| Mayumi Yuasa | 3 | 60 | 4.15 |
| Osamu Yamaguchi | 4 | 671 | 44.09 |