Transductive Transfer LDA with Riesz-based Volume LBP for Emotion Recognition in The Wild

In this paper, we propose the method using Transductive Transfer Linear Discriminant Analysis (TTLDA) and Riesz-based Volume Local Binary Patterns (RVLBP) for image based static facial expression recognition challenge of the Emotion Recognition in the Wild Challenge (EmotiW 2015). The task of this challenge is to assign facial expression labels to frames of some movies containing a face under the real word environment. In our method, we firstly employ a multi-scale image partition scheme to divide each face image into some image blocks and use RVLBP features extracted from each block to describe each facial image. Then, we adopt the TTLDA approach based on RVLBP to cope with the expression recognition task. The experiments on the testing data of SFEW 2.0 database, which is used for image based static facial expression challenge, demonstrate that our method achieves the accuracy of 50%. This result has a 10.87% improvement over the baseline provided by this challenge organizer.