TY - GEN
T1 - Face pose estimation with combined 2D and 3D HOG features
AU - Yang, Jiaolong
AU - Liang, Wei
AU - Jia, Yunde
PY - 2012
Y1 - 2012
N2 - This paper describes an approach to location and orientation estimation of a person's face with color image and depth data from a Kinect sensor. The combined 2D and 3D histogram of oriented gradients (HOG) features, called RGBD-HOG features, are extracted and used throughout our approach. We present a coarse-to-fine localization paradigm to obtain localization results efficiently using multiple HOG filters trained in support vector machines (SVMs). A feed-forward multi-layer perception (MLP) network is trained for fine face orientation estimation over a continuous range. The experimental result demonstrates the effectiveness of the RGBD-HOG feature and our face pose estimation approach.
AB - This paper describes an approach to location and orientation estimation of a person's face with color image and depth data from a Kinect sensor. The combined 2D and 3D histogram of oriented gradients (HOG) features, called RGBD-HOG features, are extracted and used throughout our approach. We present a coarse-to-fine localization paradigm to obtain localization results efficiently using multiple HOG filters trained in support vector machines (SVMs). A feed-forward multi-layer perception (MLP) network is trained for fine face orientation estimation over a continuous range. The experimental result demonstrates the effectiveness of the RGBD-HOG feature and our face pose estimation approach.
UR - http://www.scopus.com/inward/record.url?scp=84874562399&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84874562399
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2492
EP - 2495
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
Y2 - 11 November 2012 through 15 November 2012
ER -