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 chapter, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A machine learning approach and several correlation filter approaches, including our own adaptive variant, are compared. We present experimental results for 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, as well as on a realistic surveillance oriented set (SCface). The results of our experiments show that our 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.
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© 2011 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg
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Heflin, B.C., Scheirer, W.J., Rocha, A., Boult, T.E. (2011). A Look at Eye Detection for Unconstrained Environments. In: Wang, P.S.P. (eds) Pattern Recognition, Machine Intelligence and Biometrics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22407-2_15
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DOI: https://doi.org/10.1007/978-3-642-22407-2_15
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