Detection of pose orientation across single and multiple axes in case of 3D face images
This addresses pose detection for 3D facial recognition systems, but it appears incremental as it builds on existing databases and methods without claiming broad breakthroughs.
The paper tackles the problem of detecting pose orientation in 3D face images across single and multiple axes, achieving correct identification rates of 67% on FRAV3D, 80% on GAVADB, and 80% on Bosphorus databases.
In this paper, we propose a new approach that takes as input a 3D face image across X, Y and Z axes as well as both Y and X axes and gives output as its pose i.e. it tells whether the face is oriented with respect the X, Y or Z axes or is it oriented across multiple axes with angles of rotation up to 42 degree. All the experiments have been performed on the FRAV3D, GAVADB and Bosphorus database which has two figures of each individual across multiple axes. After applying the proposed algorithm to the 3D facial surface from FRAV3D on 848 3D faces, 566 3D faces were correctly recognized for pose thus giving 67% of correct identification rate. We had experimented on 420 images from the GAVADB database, and only 336 images were detected for correct pose identification rate i.e. 80% and from Bosphorus database on 560 images only 448 images were detected for correct pose identification i.e. 80%.abstract goes here.