Micro-expression recognition based on multiscale convolutional fusion and capsule network

Abstract

Due to the short duration and low intensity, the efficient feature learning is a big challenge for robust facial microexpression (ME) recognition. To achieve the diverse and spatial relation representation, this paper proposes a simple yet effective micro-expression recognition method based on multiscale convolutional fusion and capsule network (MCFCN). Firstly, the apex frame in a ME clip is located by computing the pixel difference of frames, and then the apex frame is processed by an optical flow operator. Secondly, a multi-scale fusion module is introduced to capture diverse ME related details. Then, the micro-expression features are fed into the capsule network for a good description about spatial relation. Finally, the entire ME recognition model is trained and verified on three popular benchmarks (SAMM, SMIC and CASMEII) using the associated standard evaluation protocols. Experimental results show that our method based on MCFCN is superior to the works based on pervious capsule network or other state-of-the-art CNN models.