# A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition

arXiv: https://arxiv.org/abs/2403.14318 · HTML: https://mlsanity.com/paper/2403.14318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution](https://mlsanity.com/paper/1608.01041/) (2016)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Facial Expression Recognition with Visual Transformers and Attentional Selective Fusion](https://mlsanity.com/paper/2103.16854/) (2021)
- [BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient](https://mlsanity.com/paper/2004.08495/) (2020)

## Built on by (most cited first, 1 of 1)

- [Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model](https://mlsanity.com/paper/2411.10863/) (2024)

## Abstract (arXiv)

Convolutional neural networks (CNNs) and their variations have shown effectiveness in facial expression recognition (FER). However, they face challenges when dealing with high computational complexity and multi-view head poses in real-world scenarios. We introduce a lightweight attentional network incorporating multi-scale feature fusion (LANMSFF) to tackle these issues. For the first challenge, we carefully design a lightweight network. We address the second challenge by presenting two novel components, namely mass attention (MassAtt) and point wise feature selection (PWFS) blocks. The MassAtt block simultaneously generates channel and spatial attention maps to recalibrate feature maps by emphasizing important features while suppressing irrelevant ones. In addition, the PWFS block employs a feature selection mechanism that discards less meaningful features prior to the fusion process. This mechanism distinguishes it from previous methods that directly fuse multi-scale features. Our proposed approach achieved results comparable to state-of-the-art methods in terms of parameter count and robustness to pose variation, with accuracy rates of 90.77% on KDEF, 70.44% on FER-2013, and 86.96% on FERPlus datasets. The code for LANMSFF is available at https://github.com/AE-1129/LANMSFF.