| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
[2] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 - USA
[3] Univ Maryland, Dept Elect & Comp Engn, College Pk, MD 20742 - USA
Total Affiliations: 3
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| Document type: | Journal article |
| Source: | IEEE Transactions on Image Processing; v. 21, n. 4, p. 2245-2255, APR 2012. |
| Web of Science Citations: | 52 |
| Abstract | |
With the goal of matching unknown faces against a gallery of known people, the face identification task has been studied for several decades. There are very accurate techniques to perform face identification in controlled environments, particularly when large numbers of samples are available for each face. However, face identification under uncontrolled environments or with a lack of training data is still an unsolved problem. We employ a large and rich set of feature descriptors (with more than 70 000 descriptors) for face identification using partial least squares to perform multichannel feature weighting. Then, we extend the method to a tree-based discriminative structure to reduce the time required to evaluate probe samples. The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. Experiments show that our identification method outperforms current state-of-the-art results, particularly for identifying faces acquired across varying conditions. (AU) | |
| FAPESP's process: | 10/10618-3 - Feature Combination for Analysis of Videos Involving Humans |
| Grantee: | William Robson Schwartz |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |