An Automatic Eye Detection Method for Gray Intensity Facial Images
Eyes are the most salient and stable features in the human face,
and hence automatic extraction or detection of eyes is often
considered as the most important step in many applications, such
as face identification and recognition. This paper presents a
method for eye detection of still grayscale images. The method is
based on two facts: eye regions exhibit unpredictable local
intensity, therefore entropy in eye regions is high and the center
of eye (iris) is too dark circle (low intensity) compared to the
neighboring regions. A score based on the entropy of eye and
darkness of iris is used to detect eye center coordinates.
Experimental results on two databases; namely, FERET with
variations in views and BioID with variations in gaze directions
and uncontrolled conditions show that the proposed method is
robust against gaze direction, variations in views and variety of
illumination. It can achieve a correct detection rate of 97.8% and
94.3% on a set containing 2500 images of FERET and BioID
databases respectively. Moreover, in the cases with glasses and
severe conditions, the performance is still acceptable.
Keywords: Eye detection, Iris detection, Facial features extraction, Face detection, Entropy
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