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Precise eye localisation is a crucial step for many applications, including face recognition, gaze tracking and blink detection. In this study, the authors propose several improvements to the original average of synthetic exact filters (ASEF) formulation, demonstrating that its accuracy can be enhanced if adequate illumination correction, spatial priors and cross-filter responses are exploited for eye localisation. The so-called improved ASEF (iASEF) was tested on the well-known BioID database and other more challenging datasets comprising real world face imagery: labelled faces in the wild (LFW) and the very recent labelled face parts in the wild. The iASEF provides the state-of-the-art results, ranking first on BioID database and second on a 2000-image LFW subset. In addition, the authors propose a novel, much more challenging benchmark for eye localisation using the whole LFW and a standard protocol initially designed for face verification. Improvements over original ASEF were also confirmed on this difficult test, although with a significant drop in performance. They point out the necessity of adopting these realistic validation scenarios, in order to evaluate the actual state-of-the-art and fairly compare eye localisation methods in unconstrained settings, where localisation accuracy is still far from perfect.