Rotation Axis Focused Attention Network (RAFA-Net) for Estimating Head Pose

Ardhendu Behera, Zachary Wharton, Pradeep Hewage and Swagat Kumar
Department of Computer Science, Edge Hill University, United Kingdom

Abstract

Head pose is a vital indicator of human attention and behavior. Therefore, automatic estimation of head pose from images is key to many real-world applications. In this paper, we propose a novel approach for head pose estimation from a single RGB image. Many existing approaches often predict head poses by localizing facial landmarks and then solve 2D to 3D correspondence problem with a mean head model. Such approaches completely rely on the landmark detection accuracy, an ad-hoc alignment step, and the extraneous head model. To address this drawback, we present an end-to-end deep network, which explores rotation axis (yaw, pitch, and roll) focused innovative attention mechanism to capture the subtle changes in images. The mechanism uses attentional spatial pooling from a self-attention layer and learns the importance over fine-grained to coarse spatial structures and combine them to capture rich semantic information concerning a given rotation axis. The experimental evaluation of our approach using three benchmark datasets is very competitive to state-of-the-art methods, including with and without landmark-based approaches.

RAFA-Net for estimating head poses by introducing rotation axis-specific (yaw, pitch and roll) self-attention and attentional pooling components.

Rotation Axis Focused Novel Attentional Pooling.

Paper and Supplementary Information

Open Access of the accepted paper in The CVF.

Supplementary Document

Source code

Bibtex

@inproceedings{behera2020rotation,
  title={Rotation Axis Focused Attention Network (RAFA-Net) for Estimating Head Pose},
  author={Behera, Ardhendu and Wharton, Zachary and Hewage, Pradeep and Kumar, Swagat},
  booktitle={Proceedings of the Asian Conference on Computer Vision (ACCV)},
  year={2020}
}

Acknowledgements

This research was supported by the UKIERI (CHARM) under grant DST UKIERI-2018-19-10. The GPU is kindly donated by the NVIDIA Corporation.