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

TL;DR

This paper introduces an end-to-end facial expression recognition architecture that uses a novel spatio-channel attention network and complementary context information, improving robustness to occlusions and pose variations without relying on landmark detectors.

Contribution

The work proposes a landmark-free, end-to-end FER model with a novel spatio-channel attention mechanism and a complementary context branch, enhancing robustness and performance.

Findings

Achieves superior accuracy on multiple datasets including in-the-wild and masked face datasets.

Demonstrates robustness to occlusions and pose variations.

Code is publicly available for reproducibility.

Abstract

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