In order to achieve high accuracy of face recognition, detection of facial parts such as eyes, nose, and mouth is essentially important. In this paper, we propose a method to detect eyes from frontal face images. The proposed method consists of two major steps. The first is two dimensional Hough transformation for detecting circle of unknown radius. The circular Hough transform first generates two dimensional parameter space (xc, yc) using the gradient of grayscale. The radius of circle r is determined for each local maximum in the (xc, yc) space. The second step of the proposed method is evaluation of likelihood of eye using histogram of gradient and Support Vector Machine (SVM). The eye detection step of proposed method firstly detects possible eye center by the circular Hough transform. Then it extracts histogram of gradient from rectangular window centered at each eye center. Likelihood of eye of the extracted feature vector is evaluated by SVM, and pairs of eyes satisfying predefined conditions are generated and ordered by sum of the likelihood of both eyes. Evaluation experiment is conducted using 1,409 images of the FERET database of frontal face image. The experimental result shows that the proposed method achieves 98.65% detection rate of both eyes.