HMM-based geometric signatures for compact 3D face representation and matching

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.