Key Facial Components Guided Micro-Expression Recognition Based on First & Second-Order Motion

Abstract

Although there have been many successful attempts in the field of micro-expression recognition, plenty of challenges remain due to the subtle spatio-temporal changes and high locality of micro-expressions. In this paper, to tackle such issues, we propose a novel key facial components guided micro-expression recognition approach (KFC-MER). Face semantic segmentation probability maps involving several key components provide a guidance for feature learning. With the Components-Aware Attention (CAA) module, expression-related areas are highlighted and the relationship between components will also be learned. To cope with the limited size of micro-expression datasets, we design a parallel shallow residual network as the MER network. Both the first- and second-order motion are exploited as the input data, for capturing motive information and non-rigid deformation, respectively. Extensive experiments on three benchmark datasets demonstrate that our method outperforms previous works and achieves state-of-the-art performance. The code is publicly available on GitHub: https://github.com/TJUMMG/KFC-MER.

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