Learning effective binary descriptors for micro-expression recognition transferred by macro-information

In this paper, we propose three effective binary face descriptor learning methods, namely dual-cross patterns from three orthogonal planes (DCP-TOP), hot wheel patterns (HWP) and HWP-TOP for macro/micro-expression representation. We use feature selection to make the binary descriptors compact. Because of the limited labeled micro-expression samples, we leverage abundant labeled macro-expression and speech samples to train a more accurate classifier. Coupled metric learning algorithm is employed to model the shared features between micro-expression samples and macro-information. Smooth SVM (SSVM) is selected as a classifier to evaluate the performance of micro-expression recognition. Extensive experimental results show that our proposed methods yield the state-of-the-art classification accuracies on the CASMEII database.