AutoMER: Spatiotemporal Neural Architecture Search for Microexpression Recognition

Facial microexpressions offer useful insights into subtle human emotions. This unpremeditated emotional leakage exhibits the true emotions of a person. However, the minute temporal changes in the video sequences are very difficult to model for accurate classification. In this article, we propose a novel spatiotemporal architecture search algorithm, AutoMER for microexpression recognition (MER). Our main contribution is a new parallelogram design-based search space for efficient architecture search. We introduce a spatiotemporal feature module named 3-D singleton convolution for cell-level analysis. Furthermore, we present four such candidate operators and two 3-D dilated convolution operators to encode the raw video sequences in an end-to-end manner. To the best of our knowledge, this is the first attempt to discover 3-D convolutional neural network (CNN) architectures with a network-level search for MER. The searched models using the proposed AutoMER algorithm are evaluated over five microexpression data sets: CASME-I, SMIC, CASME-II, CAS(ME) $\hat{2}$ , and SAMM. The proposed generated models quantitatively outperform the existing state-of-the-art approaches. The AutoMER is further validated with different configurations, such as downsampling rate factor, multiscale singleton 3-D convolution, parallelogram, and multiscale kernels. Overall, five ablation experiments were conducted to analyze the operational insights of the proposed AutoMER.