Capturing Head Poses Using FMCW Radar and Deep Neural Networks

• Published in: IEEE transactions on aerospace and electronic systems Vol. 61; no. 3; pp. 6748 - 6759
• Main Authors: Kumchaiseemak, Nakorn, Fioranelli, Francesco, Wilaiprasitporn, Theerawit
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Axes of rotation; Cameras; Chirp; Clutter; Continuous wave radar; Data models; Deep learning; frequency-modulated continuous wave (FMCW) radar; Head; Head movement; Indoor environments; Machine learning; Pitch (inclination); Pose estimation; Predictive models; Radar; Radar antennas; Radar data; Rolling motion; subject-specific head pose estimation; Training
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2025.3529412

This article presents the first subject-specific head pose estimation approach using only one frequency-modulated continuous wave radar data frame. Specifically, the proposed method incorporates a deep learning framework to estimate head pose rotation and orientation frame-by-frame by combining a convolutional neural network operating on range-angle radar plots and a PeakConv network. The proposed method is validated with an in-house collected dataset, including annotated head movements that varied in roll, pitch, and yaw, and these were recorded in two different indoor environments. It is shown that the proposed model can estimate head poses with a relatively small error of approximately 6.7°-14.4° for all rotational axes and is capable of generalizing to unseen, new environments when trained in one scenario (e.g., lab) and tested in another (e.g., office), including in the cabin of a car.