This paper addresses the problem of estimating head pose over a wide range of angles from low-resolution images. Faces are detected using chrominance-based features. Grey-level normalized face imageries serve as input for linear auto-associative memory. One memory is computed for each pose using a Widrow-Hoff learning rule. Head pose is classified with a winnertakes-all process. We compare results from our method with abilities of human subjects to estimate head pose from the same data set. Our method achieves similar results in estimating orientation in tilt (head nodding) angle, and higher precision for estimating orientation in the pan (side-to-side) angle. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Gourier, N., Maisonnasse, J., Hall, D., & Crowley, J. L. (2007). Head pose estimation on low resolution images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4122 LNCS, pp. 270–280). Springer Verlag. https://doi.org/10.1007/978-3-540-69568-4_24
Mendeley helps you to discover research relevant for your work.