# Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition

arXiv: https://arxiv.org/abs/2007.10298 · HTML: https://mlsanity.com/paper/2007.10298/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher](https://mlsanity.com/paper/2205.14361/) (2022)
- [PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition](https://mlsanity.com/paper/2306.09626/) (2023)
- [OUS: Scene-Guided Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2405.18769/) (2024)
- [Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition](https://mlsanity.com/paper/2109.03336/) (2021)
- [Rethinking the Learning Paradigm for Facial Expression Recognition](https://mlsanity.com/paper/2209.15402/) (2022)
- [Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation](https://mlsanity.com/paper/2106.04332/) (2021)
- [Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition](https://mlsanity.com/paper/2208.10221/) (2022)
- [Imponderous Net for Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2103.15136/) (2021)
- [A Perception CNN for Facial Expression Recognition](https://mlsanity.com/paper/2512.06422/) (2025)
- [Masked Student Dataset of Expressions](https://mlsanity.com/paper/2304.03867/) (2023)
- [Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition](https://mlsanity.com/paper/2305.01884/) (2023)
- [MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition](https://mlsanity.com/paper/2401.07245/) (2024)

## Abstract (arXiv)

A recent trend to recognize facial expressions in the real-world scenario is to deploy attention based convolutional neural networks (CNNs) locally to signify the importance of facial regions and, combine it with global facial features and/or other complementary context information for performance gain. However, in the presence of occlusions and pose variations, different channels respond differently, and further that the response intensity of a channel differ across spatial locations. Also, modern facial expression recognition(FER) architectures rely on external sources like landmark detectors for defining attention. Failure of landmark detector will have a cascading effect on FER. Additionally, there is no emphasis laid on the relevance of features that are input to compute complementary context information. Leveraging on the aforementioned observations, an end-to-end architecture for FER is proposed in this work that obtains both local and global attention per channel per spatial location through a novel spatio-channel attention net (SCAN), without seeking any information from the landmark detectors. SCAN is complemented by a complementary context information (CCI) branch. Further, using efficient channel attention (ECA), the relevance of features input to CCI is also attended to. The representation learnt by the proposed architecture is robust to occlusions and pose variations. Robustness and superior performance of the proposed model is demonstrated on both in-lab and in-the-wild datasets (AffectNet, FERPlus, RAF-DB, FED-RO, SFEW, CK+, Oulu-CASIA and JAFFE) along with a couple of constructed face mask datasets resembling masked faces in COVID-19 scenario. Codes are publicly available at https://github.com/1980x/SCAN-CCI-FER