中国科学院自动化研究所机构知识库

Micro-expression recognition is a challenging task due to its low intensity and short duration and how to extract the subtle facial changes is a key issue in this field. Although there are many methods attempt to cope with this problem, they are difficult to encode the temporal order of all frames in the video clips. For these reasons, this paper employs rank pooling and l2,1-norm to obtain the discriminative video representation with temporal order. In particular, we extract Local Two-Order Gradient Pattern (LTOGP) feature of each frame to describe the subtle information. Then, the video representation is generated by using rank pooling, which captures the temporal order among all frames. Furthermore, considering the sparsity of l2,1-norm, we can select those discriminant features. Finally, micro-expression classification is accomplished using SVM. Experiments are conducted on two publicly available micro-expression databases i.e. CASME and CASME2. The results demonstrate that our method achieves better performance than the state-of-the-art algorithms.