In this paper we address the problem of pose independent face recognition with a gallery set containing one frontal face image per enrolled subject while the probe set is composed by just a face image undergoing pose variations. The approach uses a set of aligned 3D models to learn deformation components using a 3D Morphable Model (3DMM). This further allows fitting a 3DMM efficiently on an image using a Ridge regression solution, regularized on the face space estimated via PCA. Then the approach describes each profile face by computing LBP histograms localized on each deformed vertex, projected on a rendered frontal view.
In the experimental result we evaluate the proposed method on the CMU Multi-PIE to assess face recognition algorithm across pose. We show how our process leads to higher performance than regular baselines reporting high recognition rate considering a range of facial poses in the probe set, up to -+45°. Finally we remark that our approach can handle continuous pose variations and it is comparable with recent state-of-the-art approaches.
Main work of Iacopo Masi.
Collaboration with USC, University of Southern California.