Shape alignment or estimation under occlusion is one of the most challenging tasks in computer vision field. Most previous works treat occlusion as noises or part models, which usually lead to low accuracy or inefficiencies. This paper proposes an efficient and accurate regression-based algorithm for face alignment. In this framework, local and global regressions are iteratively used to train a series of random forests in a cascaded manner. In training and testing process, each step consists of two layers. In the first layer, a set of highly discriminative local features are extracted from local regions according to locality principle. The regression forests are trained for each facial landmark independently using those local features. Then the leaf node of the regression tree is encoded by histogram statistic method and the final shape is estimated by a linear regression matrix. In the second layer, our proposed global features are generated. Then we use those features to train a random fern to keep the global shape constraints. Experiments show that our method has a high speed, but same or slightly lower accuracy than state of the art methods under occlusion condition. In order to gain a higher accuracy we use multi-random shape for initialization, which may slightly reduce the calculation efficiency as a trade-off.
['Songrui Guo', 'Guanghua Tan', 'Huawei Pan', 'Lin Chen', 'Chunming Gao']
Face alignment under occlusion based on local and global feature regression