The Community for Technology Leaders
RSS Icon
Subscribe
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-6
U. Castellani , Dipartimento di Informatica, Strada le Grazie, 15 - 37134 Verona, Italy
M. Cristani , Dipartimento di Informatica, Strada le Grazie, 15 - 37134 Verona, Italy
X. Lu , Siemens Corporate Research, College Road East Princeton - NJ 08540 USA
V. Murino , Dipartimento di Informatica, Strada le Grazie, 15 - 37134 Verona, Italy
A. K. Jain , Michigan State University, East Lansing - 48824, USA
ABSTRACT
3D face recognition(s) systems improve current 2D image-based approaches, but in general they are required to deal with larger amounts of data. Therefore, a compact representation of 3D faces is often crucial for a better manipulation of data, in the context of 3D face applications such as smart card identity verification systems. We propose a new compact 3D representation by focusing on the most significant parts of the face. We introduce a generative learning approach by adapting Hidden Markov Models (HMM) to work on 3D meshes. The geometry of local area around face fiducial points is modeled by training HMMs which provide a robust pose invariant point signature. Such description allows the matching by comparing the signature of corresponding points in a maximum-likelihood principle. We show that our descriptor is robust for recognizing expressions and performs faster than the current ICP-based 3D face recognition systems by maintaining a satisfactory recognition rate. Preliminary results on a subset of the FRGC 2.0 dataset are reported by considering subjects under different expressions.
CITATION
U. Castellani, M. Cristani, X. Lu, V. Murino, A. K. Jain, "HMM-based geometric signatures for compact 3D face representation and matching", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-6, doi:10.1109/CVPRW.2008.4563126
228 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool