Pose Independent Face Recognition by Localizing Local Binary Patterns via Deformation Components
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 Morph able 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 Local Binary Pattern (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.