The first expected outcome for this research is a face recognition technique more robust to face orientation than the current state-of-the-art.
The applications for that kind of research are mainly Security (ex: 3D CCTV) and the Human-Machine interactions (ex: vision in robotics).
Besides, the automatic landmark detection technique can help in all domains where face labelling is needed on big database, from computer vision to psychology.
Automatic landmark detection can be used in face recognition for two purposes. It can help to find correspondences between two faces before matching and it can help to extract discriminative information about the face being treated. The information can be featural and be supported by the neighbourhood of each landmark (ex: local shape around the landmarks) or configural and be linked to the relationships between them (ex: distances between landmarks).

I join the EVAN project quite recently, in October 2008. My work during this one year funded by EVAN have been for the greatest part dedicated to automatic landmark detection on faces and for a smaller part to landmark labelling and face matching.

 


For the landmark detection, I combine sets of simple fields, for example several types of curvature and volumetric information as well as crestline and isolines on the surface to detect points. The repeatability of those landmark are tested using manually landmarked datasets like the Face Recognition Grand Challenge database (FRGC).

For both landmark labelling and face matching, I construct hypergraphs upon the detected landmarks and match them on models using hypergraph matching technique by relaxation through elimination.