  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
difference 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 or better than state of the art.
