Towards Accurate Facial Landmark Detection via Cascaded Transformers

TL;DR

This paper introduces a cascaded transformer-based model for facial landmark detection that leverages self-attention and deformable attention mechanisms to improve accuracy, especially under challenging conditions like pose variations and occlusions.

Contribution

The paper proposes a novel cascaded transformer architecture with a deformable attention mechanism and a joint feature-landmark refinement decoder for improved facial landmark detection.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Demonstrates robustness under large pose and occlusion conditions.

Shows strong generalization in cross-dataset evaluations.

Abstract

Accurate facial landmarks are essential prerequisites for many tasks related to human faces. In this paper, an accurate facial landmark detector is proposed based on cascaded transformers. We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end. With self-attention in transformers, our model can inherently exploit the structured relationships between landmarks, which would benefit landmark detection under challenging conditions such as large pose and occlusion. During cascaded refinement, our model is able to extract the most relevant image features around the target landmark for coordinate prediction, based on deformable attention mechanism, thus bringing more accurate alignment. In addition, we propose a novel decoder that refines image features and landmark positions simultaneously. With few parameter increasing, the detection performance improves further. Our model achieves new state-of-the-art performance on several standard facial landmark detection benchmarks, and shows good generalization ability in cross-dataset evaluation.