Facial Micro-Expression Generation based on Deep Motion Retargeting and Transfer Learning

Xinqi Fan, Ali Raza Shahid, Hong Yan

Abstract: Facial micro-expression (FME) refers to a brief spontaneous facial movement that can reveal a person's genius emotion. One challenge in facial micro-expression is the lack of data. Fortunately, generative deep neural network models can assist in the creation of desired images. However, the issues for micro-expressions are the facial variations are too subtle to capture, and the limited training data may make feature extraction difficult. To address these issues, we developed a deep motion retargeting and transfer learning based facial micro-expression generation model (DMT-FMEG). First, to capture subtle variations, we employed a deep motion retargeting (DMR) network that can learn keypoints in an unsupervised manner, estimate motions, and generate desired images. Second, to enhance the feature extraction ability, we applied deep transfer learning (DTL) by borrowing knowledge from macro-expression images. We evaluated our method on three datasets, CASME II, SMIC, and SAMM, and found that it showed satisfactory results on all of them. With the effectiveness of the method, we won the second place in the generation task of the FME 2021 challenge.