Abstract: Head position and pose model is created. Also, a method for head poses angle estimation based on Convolution Neural Network (CNN) is proposed. 3D head position model is created from these locations and obtain 3D coordinate of head position. The method proposed here uses CNN. As for the head pose detection, OpenCV and Dlib of the open-source software tools are used with Python program. The images used were RGB images, RGB images + thermography, grayscale images, and RGB images assuming images obtained by near infrared rays, with only the red channel elements extracted. As a result, the RGB image model was the most accurate, but considering the criteria set, the RGB image model was used for morning and daytime detection, and the near-infrared image was used for nighttime and rainy weather scenes. It turned out that it is better to use the model obtained by the training in. The experimental results show almost perfect head pose detection performance when the head pose angle ranges from 0 to 180 degrees with 45 degrees steps.

Kohei Arai, Akifumi Yamashita and Hiroshi Okumura, “Head Position and Pose Model and Method for Head Pose Angle Estimation based on Convolution Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121006

@article{Arai2021,
title = {Head Position and Pose Model and Method for Head Pose Angle Estimation based on Convolution Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121006},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121006},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Kohei Arai and Akifumi Yamashita and Hiroshi Okumura}
}