Local Linear Regression (LLR) for Pose Invariant Face Recognition

Abstract

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is well known as one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given non-frontal view to obtain a virtual gallery/probe face. By formulating this kind of solutions as a prediction problem, this paper proposes a simple but efficient novel local linear regression (LLR) method, which can generate the virtual frontal view from a given non-frontal face image. The proposed LLR inspires from the observation that the corresponding local facial regions of the frontal and non-frontal view pair satisfy linear assumption much better than the whole face region. This can be explained easily by the fact that a 3D face shape is composed of many local planar surfaces, which satisfy naturally linear model under imaging projection. In LLR, we simply partition the whole non-frontal face image into multiple local patches and apply linear regression to each patch for the prediction of its virtual frontal patch. Comparing with other methods, the experimental results on CMU PIE database show distinct advantage of the proposed method