| 英文标题 | Real-time micro-expression recognition algorithm based on atrous convolutions for CNN |
| 作者英文名 | Lai Zhenyi, Chen Renhe, Qian Yurong |
| 机构英文名 | School of Software,Xinjiang University,Urumqi 830000,China |
| 英文摘要 | Based on the depth feature of facial micro-expression recognition, such as CNN, the classification method of facial micro-expression recognition is gradually improved. Compared with the traditional feature extraction method, it is easier to meet the real-time application. In order to perfect the details and extract the fine features of micro-expressions, this paper proposed a new algorithm combining the atrous convolutions kernel and the automatic correction of face to improve the feature extraction process of CNN network. It trained and tested the model on CASME and CASME Ⅱ micro-expression public data sets through real-time recognition in real-time application of automatic face correction. It improved the robustness of the model by comparing the loss function schemes. The accuracy of the method in CASME is 70.16% and witch in CASMEⅡ is 72.26%. Real-time recognition frame rate at 60 fps. This method can effectively improve the accuracy of micro-expression recognition, meet the real-time requirements, and has good robustness and generalization ability. |
| 英文关键词 | micro-expression recognition; atrous convolutions; expression recognition; CNN |