Driver face detection based on aggregate channel features and deformable part-based model in traffic camera

Abstract

We explore the problem of detecting driver faces in cabs from images taken by traffic cameras. Dim light in cabs, occlusion and low resolution make it a challenging problem. We employ aggregate channel features instead of a single feature to reduce the miss rate, which will introduce more false positives. Based on the observation that most running vehicles have a license plate and the relative position between the plate and driver face has an approximately fixed pattern, we refer to the concept of deformable part-based model and regard a candidate face and a plate as two deformable parts of a face-plate couple. A candidate face will be rejected if it has a low confidence score. Experiment results demonstrate the effectiveness of our method.