Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6940391 | Pattern Recognition Letters | 2018 | 9 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Xianye Ben, Xitong Jia, Rui Yan, Xin Zhang, Weixiao Meng,