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Eye centre localization plays a crucial role in computer vision applications like face recognition, gaze estimation, driver fatigue detection, liveness detection, etc. However, it is difficult to localize the eye centre due to the variations in pose, occlusion, illumination, specular reflection, rotation, scale, etc. This work proposes an integrated approach robust to the variations mentioned above and localizes the eye centre precisely. This integrated approach consists of (i) Faster RCNN deep learning model to detect the face and eyes; (ii) AlexNet is utilized for eye openness detection using transfer learning; (iii) Rectangular-intensity-gradient (RIG) is proposed for eye centre localization. Experimentations were performed on benchmark databases like GI4E and BioID. An accuracy of 97.50% and 94.25% for N err ≤ 0.05, 98.75% and 98.40% for N err ≤ 0.10, 99.64% and 99.45% for N err ≤ 0.25 was achieved across GI4E and BioID databases respectively. In addition, the robustness of the proposed approach was tested on AR and CAS-PEAL databases. The proposed approach of eye centre localization performs relatively better than existing state-of-the-art methods in terms of accuracy and computational time. [ABSTRACT FROM AUTHOR]

The estimation of the human eye centers is one of the important step in several computer vision applications such as driver drowsiness detection, eye tracking, face recognition etc. Most of the existing techniques are able to localize eyes in frontal faces only while they fail to localize eye pairs in complex scenarios such as changes in head pose, scale, and illumination. In this paper, an eye localization method has been proposed that can locate the eye centers more precisely in facial images captured under the above-mentioned complexities. The proposed method consists of three stages: eye candidate detection, eye candidate verification, and post-processing. In eye candidate detection, the possible eye candidates are extracted using two new features namely Semi-Circular Edge Shape (sCES) and Semi-Ellipse Edge Shape (sEES) features. These features take into consideration the semi-circular and semi-ellipse edges of iris and eyelid and hence are able to localize eye centers more precisely. In verification, the extracted eye candidates are verified using a Support Vector Machine (SVM) based classifier. A scale-space framework is also included in the verification stage to handle the scale variations of images. In post-processing, the eye centers are paired using some geometrical constraints and then a modified gradient-based method is proposed to detect the required eye pair. The proposed system is evaluated on different databases to check its robustness to changes in head pose, scale, illumination etc. The experimental results suggest that the proposed method shows better accuracy in challenging environments and also outperforms some state-of-the-art methods. • We proposed semi-circular edge shape (sCES) and semi-ellipse edge shape (sEES) features for possible eye detection. • The Hit-or-miss transform is applied to extract positive slope edge (PSE) and negative slope edge (NSE). • A scale-space framework (SSF) is integrated with the proposed method to address the scale variations of facial images. • A modified gradient-based method is proposed to localize eye center more precisely. • Experimental analyses demonstrate that our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The accurate detection and localization of an eye in a facial image is important for many computer vision applications, such as face recognition, fatigued driving detection, and gaze estimation. Although research on eye detection has matured, most existing eye detection methods produce poor performance in various practical scenarios where there exists variation in facial expressions or illumination, people wearing clear eyeglasses, and so on. We have proposed a method that can locate eyes under the above-mentioned varied environmental conditions. The proposed approach follows two steps: eye candidate detection and eye candidate verification. In the first step, two features, namely semicircular edge shape and semiellipse edge shape features, are proposed to detect the eye candidates. In the second step, all of the selected eye candidates are verified using a support vector machine trained with the fusion of local binary pattern, cell mean intensity, and histogram of oriented gradients features. The proposed method has been tested under different conditions of AR, the Chinese Academy of Sciences' Pose, Expression, Accessories, and Lighting, and the facial recognition technology databases. The experimental results suggest that the proposed method provides better performance as compared to the existing methods in terms of precision and recall. [ABSTRACT FROM AUTHOR]