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This paper introduces a class of correlation filters called average of synthetic exact filters (ASEF). For ASEF, the correlation output is completely specified for each training image. This is in marked contrast to prior methods such as synthetic discriminant functions (SDFs) which only specify a single output value per training image. Advantages of ASEF training include: insensitivity to over-fitting, greater flexibility with regard to training images, and more robust behavior in the presence of structured backgrounds. The theory and design of ASEF filters is presented using eye localization on the FERET database as an example task. ASEF is compared to other popular correlation filters including SDF, MACE, OTF, and UMACE, and with other eye localization methods including Gabor Jets and the OpenCV cascade classifier. ASEF is shown to outperform all these methods, locating the eye to within the radius of the iris …