Rotation-Invariant Facial Feature Detection Using Gabor Wavelet and Entropy

A novel technique for facial feature detection in images of frontal faces is presented. We use a set of Gabor wavelet coefficients in different orientations and frequencies to analyze and describe facial features. However, due to the lack of sufficient local structures for describing facial features, Gabor wavelets can not perfectly capture the wide range of possible variations in the appearance of facial features, and thus can give many false positive (and sometimes false negative) responses. We show that the performance of such a feature detector can be significantly improved by using the local entropy of features. Complex regions in a face image, such as the eye, exhibit unpredictable local intensity and hence high entropy. Our method is robust against image rotation, varying brightness, varying contrast and a certain amount of scaling.

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