Name
Abstract | ||
|---|---|---|
Face recognition from video has recently received much interest. However, several challenges for such a system exist, such as resolution, occlusion (from objects or self-occlusion), motion blur, and illumination. The aim of this paper is to overcome the problem of self-occlusion by observing a person from multiple cameras with uniquely different views of the person's face and fusing the recognition results in a meaningful way. Each camera may only capture a part of the face, such as the right or left half of the face. We propose a methodology to use cylinder head models (CHMs) to track the face of a subject in multiple cameras. The problem of face recognition from video is then transformed to a still face recognition problem which has been well studied. The recognition results are fused based on the extracted pose of the face. For instance, the recognition result from a frontal face should be weighted higher than the recognition result from a face with a yaw of 30°. Eigenfaces is used for still face recognition along with the average-half-face to reduce the effect of transformation errors. Results of tracking are further aggregated to produce 100% accuracy using video taken from two cameras in our lab. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WACV.2009.5403055 | Applications of Computer Vision |
Keywords | Field | DocType |
face recognition,hidden feature removal,image resolution,pose estimation,tracking,video cameras,Eigenfaces,average-half-face,cylinder head models,fusing face recognition,pose extraction,self-occlusion,still face recognition,tracking,transformation errors,video cameras | Computer vision,Facial recognition system,Eigenface,3D single-object recognition,Object-class detection,Three-dimensional face recognition,Pattern recognition,Computer science,Motion blur,Pose,Artificial intelligence,Face detection | Conference |
ISSN | ISBN | Citations |
1550-5790 | 978-1-4244-5497-6 | 10 |
PageRank | References | Authors |
0.54 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Josh Harguess | 1 | 10 | 0.54 |
| Changbo Hu | 2 | 613 | 34.71 |
| Aggarwal, J.K. | 3 | 19 | 2.45 |