Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment
This work improves head pose estimation accuracy for applications like human-computer interaction, though it appears incremental.
The paper tackles head pose estimation from RGB images by adjusting face bounding box margins and selecting appropriate loss functions, achieving new state-of-the-art results on standard benchmark datasets.
We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation.