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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
HMM-based geometric signatures for compact 3D face representation and matching
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
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
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, pp.1-6, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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