Abstract
This paper introduces our head pose estimation system that localizes nose-tip of the faces and estimate head poses in studio quality pictures. After the nose-tip in the training data are manually labeled, the appearance variation caused by head pose changes is characterized by tensor model. Given images with unknown head pose and nose-tip location, the nose-tip of the face is localized in a coarse-to-fine fashion, and the head pose is estimated simultaneously by the head pose tensor model. The image patches at the localized nose tips are then cropped and sent to two other head pose estimators based on LEA and PCA techniques. We evaluated our system on the Pointing’04 head pose image database. With the nose-tip location known, our head pose estimators can achieve 94~96% head pose classification accuracy(within ±15o). With nose-tip unknown, we achieves 85% nose-tip localization accuracy(within 3 pixels from the ground truth), and 81~84% head pose classification accuracy(within ±15o).
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© 2007 Springer Berlin Heidelberg
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Tu, J., Fu, Y., Hu, Y., Huang, T. (2007). Evaluation of Head Pose Estimation for Studio Data. In: Stiefelhagen, R., Garofolo, J. (eds) Multimodal Technologies for Perception of Humans. CLEAR 2006. Lecture Notes in Computer Science, vol 4122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69568-4_25
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DOI: https://doi.org/10.1007/978-3-540-69568-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69567-7
Online ISBN: 978-3-540-69568-4
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