Cascade support vector regression-based facial expression-aware face frontalization

The main aim of face frontalization is to synthesize the frontal facial appearances from non-frontal facial images. How to estimate the frontal face-shape is a crucial but very challenging problem in the frontalization task. Most existing methods use a single shape template to fit in with frontal facial appearances, which will result in a loss of expression-related information. In this work, we present a novel facial expression-aware face frontalization method which directly learns the pair-wise relations between non-frontal face-shape and its frontal counterpart. The support vector regression is explored to train the pair-wise regression model. Considered the pair-wise relationship is non-linear, an appropriate cascade manner is applied to iteratively adjust and optimize the model. With the estimated frontal shape, facial appearances are synthesized through a texture-fitting process formulated by solving a simple optimization problem. The proposed method has been evaluated on a in-the-wild facial expression database. The experimental results shows an outstanding performance of both visual effects of expression recovery and facial expression recognition.