Real-time accurate eye center localization for low-resolution grayscale images

Eye center localization is considered a crucial step for many human–computer interaction (HCI) real-time applications. Detecting the center of eye (COE), accurately and in real time, is very challenging due to the wide variation of poses, eye appearance and specular reflection, especially in low-resolution images. In this paper, an accurate real-time detection algorithm of the COE is proposed. The proposed approach depends on the image gradient to detect the COE. The computational complexity is minimized and the accuracy is improved by down sampling the face resolution and applying a rough-to-fine algorithms, to reduce the search area, in accordance with the Eye Region Of Interest (EROI) and the number of COE candidates, tested by the proposed algorithm. Also, the detection algorithm is applied on a limited number of pixels that represent the iris boundary of the COE candidates. The Look Up Tables (LUTs) are implemented to, initially, store the invariant elements of the proposed image gradient-based algorithm, to reduce the detection time. Before applying the proposed COE detection approach, a modified specular reflection method is used to improve the detection accuracy. The performance of the proposed algorithm has been evaluated by applying it to three benchmark databases: the BIOID, GI4E and Talking Face video datasets, at different face resolutions. Experimental results revealed that the accuracy of the proposed algorithm is up to 91.68% and 96.7% for BIOID and GI4E datasets, respectively, while the minimum achieved average detection time is 2.7 ms. The promising results highlight the potential of the proposed algorithm to be used in some eye gaze-based real-time applications. Comparing the proposed method with the most state-of-the-art approaches showed that the system outperforms most of them and has a comparable performance with the others, in terms of the COE localization accuracy and detection speed.