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.