Name
Abstract | ||
|---|---|---|
In this paper, we address the use of three dimensional facial shape information for human face identification. We propose a new method to represent faces as 3D registered point clouds. Fine registration of facial surfaces is done by first automatically finding important facial landmarks and then, establishing a dense correspondence between points on the facial surface with the help of a 3D face template-aidedthin plate spline algorithm. After the registration of facial surfaces, similarity between two faces is defined as a discrete approximation of the volume difference between facial surfaces. Experiments done on the 3D RMA dataset show that the proposed algorithm performs as good as the point signature method, and it is statistically superior to the point distribution model-based method and the 2D depth imagery technique. In terms of computational complexity, the proposed algorithm is faster than the point signature method. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICPR.2004.1333734 | Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference |
Keywords | Field | DocType |
approximation theory,computational complexity,computer vision,face recognition,computational complexity,discrete approximation,face recognition,point signature method,registered facial surfaces,three-dimensional facial shape | Facial recognition system,Point distribution model,Computer vision,Face hallucination,Thin plate spline,Pattern recognition,Three-dimensional face recognition,Computer science,Approximation theory,Artificial intelligence,Point cloud,Computational complexity theory | Conference |
Volume | ISSN | ISBN |
4 | 1051-4651 | 0-7695-2128-2 |
Citations | PageRank | References |
37 | 1.64 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M Okan İrfanoğlu | 1 | 182 | 11.04 |
| Berk Gokberk | 2 | 113 | 6.23 |
| lale akarun | 3 | 1201 | 70.68 |