Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||Difficult Detection: A Comparison Of Two Different Approaches To Eye Detection For Unconstrained Environments|
|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. ©2009 IEEE.|
|Citation:||Ieee 3rd International Conference On Biometrics: Theory, Applications And Systems, Btas 2009. , v. , n. , p. - , 2009.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.