| Author(s): |
Scheirer, Walter J.
;
Rocha, Anderson
;
Heflin, Brian
;
Boult, Terrance E.
;
IEEE
Total Authors: 5
|
| Document type: | Journal article |
| Source: | 2009 IEEE 3RD INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS; v. N/A, p. 2-pg., 2009-01-01. |
| Abstract | |
Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A new machine learning approach and several correlation filter approaches, including a new adaptive variant, are compared. We present experimental results on a variety of controlled data sets (derived from FERET and CMU PIE) that have been re-imaged under the difficult conditions of interest with an EMCCD based acquisition system. The results of our experiments show that our new detection approaches are extremely accurate under all tested conditions, and significantly improve detection accuracy compared to a leading commercial detector. This unique evaluation brings us one step closer to a better solution for the unconstrained face recognition problem. (AU) | |
| FAPESP's process: | 08/08681-9 - Digital Image Forensics: Forgery and spoofing detection |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |