Shallow convolution and attention-based models for micro-expression recognition

The use of deep learning models for micro-expression recognition is challenging because of the absence of large datasets.This paper proposes the construction of shallow models to address this problem.It explores block-wise 3D convolutions, 2D convolutions over frames and 2D convolutional long short-term memory (ConvLSTM) over the video and combines them with multi-headed self-attention to obtain three different models.The evaluation indicates that the proposed 2D ConvLSTM and attention-based model performs the best, beating the state-of-the-art by obtaining an accuracy of 74%.It also has a parameter size that is 10 times smaller than the state-of-the-art.