Emotion Recognition with Facial Attention and Objective Activation Functions

TL;DR

This paper explores how integrating attention mechanisms and novel activation functions into CNN models enhances facial emotion recognition accuracy.

Contribution

It introduces the combination of channel and spatial attention modules with various CNN architectures and activation functions for improved emotion recognition.

Findings

Attention mechanisms significantly boost model performance.

Combining attention with different activation functions further improves results.

Enhanced models outperform baseline CNNs on emotion recognition tasks.

Abstract

In this paper, we study the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM, to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task. We show that not only attention can significantly improve the performance of these models but also that combining them with a different activation function can further help increase the performance of these models.