# EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection

arXiv: https://arxiv.org/abs/1702.02925 · HTML: https://mlsanity.com/paper/1702.02925/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2017.

## Built on by (most cited first, 26 of 26)

- [Pose-disentangled Contrastive Learning for Self-supervised Facial Representation](https://mlsanity.com/paper/2211.13490/) (2022)
- [Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation](https://mlsanity.com/paper/2210.15878/) (2022)
- [Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding](https://mlsanity.com/paper/2304.00058/) (2023)
- [ExpLLM: Towards Chain of Thought for Facial Expression Recognition](https://mlsanity.com/paper/2409.02828/) (2024)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Representation Learning and Identity Adversarial Training for Facial Behavior Understanding](https://mlsanity.com/paper/2407.11243/) (2024)
- [Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition](https://mlsanity.com/paper/2204.07935/) (2022)
- [Self-supervised Facial Action Unit Detection with Region and Relation Learning](https://mlsanity.com/paper/2303.05708/) (2023)
- [Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection](https://mlsanity.com/paper/2209.12244/) (2022)
- [Multi-label Transformer for Action Unit Detection](https://mlsanity.com/paper/2203.12531/) (2022)
- [FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection](https://mlsanity.com/paper/2212.00773/) (2022)
- [Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection](https://mlsanity.com/paper/2303.10644/) (2023)
- [Meta Auxiliary Learning for Facial Action Unit Detection](https://mlsanity.com/paper/2105.06620/) (2021)
- [Multi-Order Networks for Action Unit Detection](https://mlsanity.com/paper/2202.00446/) (2022)
- [Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers](https://mlsanity.com/paper/2203.11441/) (2022)
- [Contrastive Learning of Person-independent Representations for Facial Action Unit Detection](https://mlsanity.com/paper/2403.03400/) (2024)
- [Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis](https://mlsanity.com/paper/2203.12570/) (2022)
- [Vision Transformer for Action Units Detection](https://mlsanity.com/paper/2303.09917/) (2023)
- [Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition](https://mlsanity.com/paper/2102.12154/) (2021)
- [Feature Pyramid Network for Multi-task Affective Analysis](https://mlsanity.com/paper/2107.03670/) (2021)
- [Hierarchical Vision-Language Interaction for Facial Action Unit Detection](https://mlsanity.com/paper/2602.14425/) (2026)
- [Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors](https://mlsanity.com/paper/2210.06002/) (2022)
- [Occlusion-Robust FAU Recognition by Mining Latent Space of Masked Autoencoders](https://mlsanity.com/paper/2212.04029/) (2022)
- [AuE-IPA: An AU Engagement Based Infant Pain Assessment Method](https://mlsanity.com/paper/2212.04764/) (2022)
- [Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling](https://mlsanity.com/paper/2205.08787/) (2022)
- [Attention Based Relation Network for Facial Action Units Recognition](https://mlsanity.com/paper/2210.13988/) (2022)

## Abstract (arXiv)

In this paper, we propose a deep learning based approach for facial action unit detection by enhancing and cropping the regions of interest. The approach is implemented by adding two novel nets (layers): the enhancing layers and the cropping layers, to a pretrained CNN model. For the enhancing layers, we designed an attention map based on facial landmark features and applied it to a pretrained neural network to conduct enhanced learning (The E-Net). For the cropping layers, we crop facial regions around the detected landmarks and design convolutional layers to learn deeper features for each facial region (C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and Cropping (EAC) Net, which can learn both feature enhancing and region cropping functions. Our approach shows significant improvement in performance compared to the state-of-the-art methods applied to BP4D and DISFA AU datasets.