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In this paper, we take a look at an enhanced approach for eye detection under difficult acquisition circumstances such as low-light, distance, pose variation, and blur. We present a novel correlation filter based eye detection pipeline that is specifically designed to reduce face alignment errors, thereby increasing eye localization accuracy and ultimately face recognition accuracy. The accuracy of our eye detector is validated using data derived from the Labeled Faces in the Wild (LFW) and the Face Detection on Hard Datasets Competition 2011 (FDHD) sets. The results on the LFW dataset also show that the proposed algorithm exhibits enhanced performance, compared to another correlation filter based detector, and that a considerable increase in face recognition accuracy may be achieved by focusing more effort on the eye localization stage of the face recognition process. Our results on the FDHD dataset show that our eye detector exhibits superior performance, compared to 11 different state-of-the-art algorithms, on the entire set of difficult data without any per set modifications to our detection or preprocessing algorithms. The immediate application of eye detection is automatic face recognition, though many good applications exist in other areas, including medical research, training simulators, communication systems for the disabled, and automotive engineering.