Name
Abstract | ||
|---|---|---|
A new face recognition algorithm is proposed which is robust to variations in pose, expression and illumination. The framework is similar to the ubiquitous block matching algorithm used for motion estimation in video compression but has been adapted to compensate for illumination differences. One of the key differentiators of this approach is that unlike traditional face recognition algorithms, the image data representing the face or features extracted from the facial data is not used for classification. Instead, the mapping between the probe and gallery images given by the block matching algorithm is used to classify the faces for recognition. Once the mappings are found for each gallery image, the degree of bijectivity that each mapping produces is used to derive the similarity scores for recognition. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CVPR.2005.564 | CVPR Workshops |
Field | DocType | Volume |
Block-matching algorithm,Computer science,Artificial intelligence,Face detection,Motion estimation,Computer vision,Facial recognition system,3D single-object recognition,Object-class detection,Pattern recognition,Three-dimensional face recognition,Algorithm,Feature extraction | Conference | 2005 |
Issue | ISSN | ISBN |
1 | 2160-7508 | 0-7695-2372-2-3 |
Citations | PageRank | References |
3 | 0.45 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christine Podilchuk | 1 | 66 | 9.30 |
| Ankur Patel | 2 | 55 | 4.23 |
| Ashwath Harthattu | 3 | 6 | 0.89 |
| Saket Anand | 4 | 87 | 9.36 |
| Richard Mammone | 5 | 14 | 1.83 |