TY - GEN
T1 - Identity- and illumination-robust head pose estimation using manifold learning
AU - Wang, Chao
AU - Song, Xubo
PY - 2012
Y1 - 2012
N2 - Head pose estimation using manifold learning is challenging due to other appearance variations such as identity and illumination changes. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. Most manifold learning algorithms have two common variables: inter-point distances and graph weights, which can greatly affect the property of the constructed manifold. We propose to redefine these variables by constraining them with the pose angle information. In addition, since the environmental illuminations are distributed in the low-frequency component of the image and the texture-based feature is irrelevant to the pose variation, we use the proposed Localized Edge Orientation Histogram (LEOH) rather than the pixel intensity feature for manifold learning. The experimental results show that our method has the highest estimating accuracy and is robust to identity and illumination.
AB - Head pose estimation using manifold learning is challenging due to other appearance variations such as identity and illumination changes. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. Most manifold learning algorithms have two common variables: inter-point distances and graph weights, which can greatly affect the property of the constructed manifold. We propose to redefine these variables by constraining them with the pose angle information. In addition, since the environmental illuminations are distributed in the low-frequency component of the image and the texture-based feature is irrelevant to the pose variation, we use the proposed Localized Edge Orientation Histogram (LEOH) rather than the pixel intensity feature for manifold learning. The experimental results show that our method has the highest estimating accuracy and is robust to identity and illumination.
KW - Graph weight
KW - Inter-point distance
KW - Localized edge orientation histogram
KW - Robust head pose estimation
KW - Supervised manifold learning
UR - http://www.scopus.com/inward/record.url?scp=84873280356&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84873280356
SN - 9781601322258
T3 - Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
SP - 535
EP - 540
BT - Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
T2 - 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Y2 - 16 July 2012 through 19 July 2012
ER -