MERANet: Facial Micro-Expression Recognition using 3D Residual Attention Network
This work aims to improve the accuracy of facial micro-expression recognition for affective computing, an incremental improvement for researchers in this specific domain.
This paper addresses the challenge of facial micro-expression recognition, which is hindered by the subtle nature of these expressions and limited datasets. The authors propose MERANet, a 3D residual attention network, which achieves superior performance compared to state-of-the-art methods on benchmark datasets.
Micro-expression has emerged as a promising modality in affective computing due to its high objectivity in emotion detection. Despite the higher recognition accuracy provided by the deep learning models, there are still significant scope for improvements in micro-expression recognition techniques. The presence of micro-expressions in small-local regions of the face, as well as the limited size of available databases, continue to limit the accuracy in recognizing micro-expressions. In this work, we propose a facial micro-expression recognition model using 3D residual attention network named MERANet to tackle such challenges. The proposed model takes advantage of spatial-temporal attention and channel attention together, to learn deeper fine-grained subtle features for classification of emotions. Further, the proposed model encompasses both spatial and temporal information simultaneously using the 3D kernels and residual connections. Moreover, the channel features and spatio-temporal features are re-calibrated using the channel and spatio-temporal attentions, respectively in each residual module. Our attention mechanism enables the model to learn to focus on different facial areas of interest. The experiments are conducted on benchmark facial micro-expression datasets. A superior performance is observed as compared to the state-of-the-art for facial micro-expression recognition on benchmark data.