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This study presents a new method for precise detection of frontal human faces and eyes using a multi-level ellipse detector combined with a support vector machines verifier. Main contribution of this study lies in improving the accuracy of eye detection in high-quality images, which is often neglected by alternative methods. Although many approaches to face detection have been proposed recently, relatively little attention has been paid to the detection precision. It is worth noting that the detection precision is particularly important for face analysis purposes. More specifically, the authors demonstrate that the detection error propagation substantially affects the face recognition performance. With the proposed improvements the authors have managed to increase the face recognition rate by 7.7% for AR database compared with the publicly-available implementation of the well-established Viola-Jones face and eye detector.