Neural Networks, IEEE - INNS - ENNS International Joint Conference on (2009)
Atlanta, Ga, USA
June 14, 2009 to June 19, 2009
Quan Ju , Department of Computer Science, The University of York, Heslington, UK
Simon O'keefe , Department of Computer Science, The University of York, Heslington, UK
Jim Austin , Department of Computer Science, The University of York, Heslington, UK
In this paper, a methodology for facial feature identification and localization approach is proposed based on binary neural network algorithms. We present a head pose and facial expression invariant 3D shape descriptor called Mesh-like Multi Circle Curvature Descriptor (MMCCD), which provides more 3D curvature attributes than other similar approaches. To search and match the feature patterns with more attributes, we use Advanced Uncertain Reasoning Architecture (AURA) k-Nearest Neighbour algorithms to encode, train and match the feature patterns based on 3D shape curvature. Experiments performed on the FRGC dataset (4950 3D faces) with pose and expression variations show that our approach is able to achieve an accurate (over 99.69% nose tip identification) and robust identification and localization of facial features.
J. Austin, S. O'keefe and Quan Ju, "Binary neural network based 3D facial feature localization," Neural Networks, IEEE - INNS - ENNS International Joint Conference on(IJCNN), Atlanta, Ga, USA, 2009, pp. 1462-1469.