Face and Landmark Detection by Using Cascades of Classifiers
Résumé
In this paper, we consider face detection along with facial landmark localization inspired by the recent studies showing that incorporating object parts improves the detection accuracy. To this end, we train roots and parts detectors where the roots detector returns candidate image regions that cover the entire face, and the parts detector searches for the landmark locations within the candidate region. We use a cascade of binary and one-class type classifiers for the roots detection and SVM like learning algorithm for the parts detection. Our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the second best result on all tested challenging face detection databases. Experimental results show that including parts improves the detection performance when face images are large and the details of eyes and mouth are clearly visible, but does not introduce any improvement when the images are small.