Computational efficient deep neural network with difference attention maps for facial action unit detection

TL;DR

This paper introduces a computationally efficient deep neural network with difference image-based spatial attention maps for facial action unit detection, achieving superior accuracy with reduced complexity and faster speed.

Contribution

The paper presents a novel end-to-end deep neural network architecture that incorporates difference image-based spatial attention maps and group convolution to enhance efficiency and accuracy in AU detection.

Findings

Outperforms traditional methods on DISFA+ and CK+ datasets.

Achieves better results than state-of-the-art AU detection methods.

Maintains small model size and fast inference speed.

Abstract

In this paper, we propose a computational efficient end-to-end training deep neural network (CEDNN) model and spatial attention maps based on difference images. Firstly, the difference image is generated by image processing. Then five binary images of difference images are obtained using different thresholds, which are used as spatial attention maps. We use group convolution to reduce model complexity. Skip connection and $\text{1}\times \text{1}$ convolution are used to ensure good performance even if the network model is not deep. As an input, spatial attention map can be selectively fed into the input of each block. The feature maps tend to focus on the parts that are related to the target task better. In addition, we only need to adjust the parameters of classifier to train different numbers of AU. It can be easily extended to varying datasets without increasing too much computation. A large number of experimental results show that the proposed CEDNN is obviously better than the traditional deep learning method on DISFA+ and CK+ datasets. After adding spatial attention maps, the result is better than the most advanced AU detection method. At the same time, the scale of the network is small, the running speed is fast, and the requirement for experimental equipment is low.