A Look at Eye Detection for Unconstrained Environments A Look at Eye Detection for Unconstrained Environments

Eye detection is a necessary processing step for many face recognition algorithms. For some of these algorithms, the eye coordinates are required for proper geometric normalization before recognition. For others, the eyes serve as reference points to locate other significant features on the face, such as the nose and mouth. The eyes, containing significant discriminative information, can even be used by themselves as features for recognition. 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 will take a look at eye detection for the latter, which encompasses problems of flexible authentication, surveillance, and intelligence collection. A multitude of problems affect the acquisition of face imagery in unconstrained environments, with major problems related to lighting, distance, motion and pose. Existing work on lighting [1, 2] has focused on algorithmic issues (specifically, normalization), and not the direct impact of acquisition. Under difficult acquisition