Name
Abstract | ||
|---|---|---|
Under the occlusion of the face in an image, the existing discriminative or generative methods often fail to localize facial points because of the limitations of local facial point detectors and appearance modeling in the discriminative and generative methods, respectively. To solve this problem, we propose a new facial point localization method that combines the discriminative and generative methods. The proposed method consists of an initialization stage and optimization stage. The initialization stage detects the face, estimates the facial pose, and obtains the initial parameter set by locating the pose-specific mean shape on the detected face. The optimization stage obtains the facial points by updating the parameter set using the combined Hessian matrix and gradient vector of shape and appearance errors obtained from two methods. In experiments, the proposed method yields more accurate facial point localization under heavy occlusions and pose variations than the existing methods. |
Year | Venue | Field |
|---|---|---|
2014 | ADVANCES IN VISUAL COMPUTING (ISVC 2014), PT II | Point localization,Computer vision,Occlusion,Pattern recognition,Computer science,Hessian matrix,Active appearance model,Artificial intelligence,Initialization,Appearance modeling,Discriminative model |
DocType | Volume | ISSN |
Conference | 8888 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jongju Shin | 1 | 53 | 6.13 |
| Ji Eun Kim | 2 | 119 | 13.92 |
| Daijin Kim | 3 | 1882 | 126.85 |