Face Detection with a 3D Model

Understand

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.