Face detection by learned affine correspondences

Abstract

We propose a novel framework for detecting human faces based on correspondences between triplets of detected local features and their counterparts in an affine invariant face appearance model.Th e method is robust to partial occlusion, feature detector failure and copes well with cluttered background.B oth the appearance and configuration probabilities are learned from examples.Th e method was tested on the XM2VTS database and a limited number of images with cluttered background with promising results – 2% false negative rate – was obtained.