Abstract
This paper presents a part-based face detection approach where the spatial relationship
between the face parts is represented by a hidden 3D model with six parameters. The
computational complexity of the search in the six dimensional pose space is addressed
by proposing meaningful 3D pose candidates by image-based regression from detected
face keypoint locations. The 3D pose candidates are evaluated using a parameter sensitive
classifier based on Local Binary features relative to the 3D pose. A compatible subset of
candidates is then obtained by non-maximal suppression. Experiments on two standard
face detection datasets show that the proposed 3D model based approach obtains results
comparable to state of the art.
