Advance in Head Pose Estimation from Low Resolution Images: A Review
In the task of analyzing image of individuals, head pose is one of the most vital pieces of information ones can acquired from the image. Thus, several researchers have attempted to find a way to automatically extract such information with sufficient accuracy. On the other hand, due to the increasing of surveillance system which generally produce head images in low resolution, many researchers has shifted their focus to techniques that would applicable to low resolution environment. As a result, there are several promising works in this area. In this article, difficulties of head pose estimation from low resolution images will be discussed along with an organized survey describing the development of the field. The survey will describe characteristic papers categorized according to their underlying techniques. In each category, advantages and limitations will be discussed followed by their potential to function in practical application. Finally, some potential research topics will be discussed.
Keywords: Low-resolution head pose estimation, human-computer interfaces, low-resolution images, surveillance system
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ABOUT THE AUTHOR
Teera Siriteerakul
Teera Siriteerakul received M.S. Applied Mathematics in 2001 and M.S. Information Technology in 2003 from Rensselaer Polytechnic Institute, USA. Since 2006, he joined the Faculty of Science at King Mongkut’s Institute of Technology Ladkrabang in the Department of Computer Science as a Lecturer. His research activities include Computer Vision, Image Processing, and Pattern Recognition techniques relevance to Computer Vision.
Teera Siriteerakul
Teera Siriteerakul received M.S. Applied Mathematics in 2001 and M.S. Information Technology in 2003 from Rensselaer Polytechnic Institute, USA. Since 2006, he joined the Faculty of Science at King Mongkut’s Institute of Technology Ladkrabang in the Department of Computer Science as a Lecturer. His research activities include Computer Vision, Image Processing, and Pattern Recognition techniques relevance to Computer Vision.
