Face Detection with a 3D Model
This work addresses face detection for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles face detection by using a hidden 3D model to represent spatial relationships between face parts, achieving results comparable to or better than state-of-the-art on standard datasets.
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.