Eye correction using correlation information

Abstract

This paper proposes a novel eye detection method using the MCT-based pattern correlation. The proposed method detects the face by the MCT-based AdaBoost face detector over the input image and then detects two eyes by the MCT-based AdaBoost eye detector over the eye regions. Sometimes, we have some incorrectly detected eyes due to the limited detection capability of the eye detector. To reduce the falsely detected eyes, we propose a novel eye verification method that employs the MCT-based pattern correlation map. We verify whether the detected eye patch is eye or non-eye depending on the existence of a noticeable peak. When one eye is correctly detected and the other eye is falsely detected, we can correct the falsely detected eye using the peak position of the correlation map of the correctly detected eye. Experimental results show that the eye detection rate of the proposed method is 98.7% and 98.8% on the Bern images and AR-564 images. © Springer-Verlag Berlin Heidelberg 2007.