TY - GEN
T1 - Evaluation of head pose estimation for studio data
AU - Tu, Jilin
AU - Fu, Yun
AU - Hu, Yuxiao
AU - Huang, Thomas
PY - 2007
Y1 - 2007
N2 - 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 ±15°). 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 ±15°).
AB - 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 ±15°). 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 ±15°).
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U2 - 10.1007/978-3-540-69568-4_25
DO - 10.1007/978-3-540-69568-4_25
M3 - Conference contribution
AN - SCOPUS:38049172644
SN - 9783540695677
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 281
EP - 290
BT - Multimodal Technologies for Perception of Humans - First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR 2006 Revised Selected Papers
PB - Springer
T2 - 1st International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR 2006
Y2 - 6 April 2006 through 7 April 2006
ER -