Cascaded Multi-level Transformed Dirichlet Process for Multi-pose Facial Expression Recognition
Qirong Mao, Feifei Zhang, Liangjun Wang, Sidian Luo, Ming Dong · The Computer Journal · 2018
As an essential way of human emotional behavior understanding, facial expression recognition (FER) has been studied extensively in recent years. However, the existing methods of FER are typically based on near-frontal face data. High-recognition accuracy for multi-pose FER continues to be a challenge. In this paper, we present a novel cascaded multi-level Transformed Dirichlet Process (cml-TDP) model for multi-pose FER. The top-level structure of the cml-TDP model has been carefully designed to make coarse-to-fine prediction, and the outputs of the model are fused for robust and accurate estimation at each level. There are three primary merits to cml-TDP. First, pose is explicitly introduced into cml-TDP so that separate training and parameter tuning for each pose is not required. Second, cml-TDP describes an image by its detected positions and appearance features to implicitly construct geometric constraints. Third, cml-TDP can learn an intermediate facial expression representation subject to geometric constraints. By sharing the pool of spatially coherent features over expressions and poses, we provide a scalable solution for multi-pose FER. The proposed model has been evaluated on two benchmark databases, BU-3DFE and RAFD, and achieved 79.33% and 75.00% FER accuracy on these two datasets, respectively, which has outperformed current state-of-the-art FER methods.