Heatmap Regression without Soft-Argmax for Facial Landmark Detection
Abstract
Facial landmark detection is an important task in computer vision with numerous applications, such as head pose estimation, expression analysis, face swapping, etc. Heatmap regression-based methods have been widely used to achieve state-of-the-art results in this task. These methods involve computing the argmax over the heatmaps to predict a landmark. Since argmax is not differentiable, these methods use a differentiable approximation, Soft-argmax, to enable end-to-end training on deep-nets. In this work, we revisit this long-standing choice of using Soft-argmax and demonstrate that it is not the only way to achieve strong performance. Instead, we propose an alternative training objective based on the classic structured prediction framework. Empirically, our method achieves state-of-the-art performance on three facial landmark benchmarks (WFLW, COFW, and 300W), converging $2.2\times$ faster during training while maintaining better/competitive accuracy.
Evaluation
- Please check the instruction in our GitHub.
WFLW
bash scripts/test/WFLW.sh WFLW/best_model.pkl
- NME: 3.97
- FR: 1.96
- AUC: 0.608
COFW
bash scripts/test/COFW.sh COFW/best_model.pkl
- NME 4.54
- FR 0.62
- AUC 0.547
300W
bash scripts/test/300W.sh 300W/best_model.pkl
- NME: 2.86
- FR: 4.91
- AUC: 0.441
Citation
@inproceedings{yang2025regression,
title={Heatmap Regression without Soft-Argmax for Facial Landmark Detection},
author={Yang, Chiao-An and Yeh, Raymond A},
booktitle={Proc. ICCV},
year={2025}
}
Contact
Please contact Chiao-An Yang if you have any questions.