# Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks

## TL;DR

This paper introduces two 3D convolutional neural network models, MicroExpSTCNN and MicroExpFuseNet, designed to recognize spontaneous facial micro-expressions by capturing spatiotemporal features, outperforming existing methods on benchmark datasets.

## Contribution

The paper proposes novel 3D-CNN architectures specifically for micro-expression recognition, utilizing full facial and regional feature fusion to improve accuracy.

## Key findings

- MicroExpSTCNN outperforms previous methods on CAS(ME)^2 and SMIC datasets.
- MicroExpFuseNet effectively fuses eye and mouth regions for better recognition.
- Proposed models demonstrate significant accuracy improvements over state-of-the-art approaches.

## Abstract

Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the other hand, facial micro-expressions generally represent the actual emotion of a person, as it is a spontaneous reaction expressed through human face. Despite of a few attempts made for recognizing micro-expressions, still the problem is far from being a solved problem, which is depicted by the poor rate of accuracy shown by the state-of-the-art methods. A few CNN based approaches are found in the literature to recognize micro-facial expressions from still images. Whereas, a spontaneous micro-expression video contains multiple frames that have to be processed together to encode both spatial and temporal information. This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework. The MicroExpSTCNN considers the full spatial information, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion of the eyes and mouth regions. The experiments are performed over CAS(ME)^2 and SMIC micro-expression databases. The proposed MicroExpSTCNN model outperforms the state-of-the-art methods.