TY - GEN
T1 - 3D head pose estimation with convolutional neural network trained on synthetic images
AU - Liu, Xiabing
AU - Liang, Wei
AU - Wang, Yumeng
AU - Li, Shuyang
AU - Pei, Mingtao
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/3
Y1 - 2016/8/3
N2 - In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering techniques, in which we consider the variation of gender, age, race and expression. Our dataset includes 74000 head poses rendered from 37 head models. For each head pose, RGB image and annotated pose parameters are given. We evaluate our method on both synthetic and real data. The experiments show that our method improves the accuracy of head pose estimation.
AB - In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering techniques, in which we consider the variation of gender, age, race and expression. Our dataset includes 74000 head poses rendered from 37 head models. For each head pose, RGB image and annotated pose parameters are given. We evaluate our method on both synthetic and real data. The experiments show that our method improves the accuracy of head pose estimation.
KW - Convolutional neural network
KW - Head pose estimation
KW - Synthetic images
UR - http://www.scopus.com/inward/record.url?scp=85006736322&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7532566
DO - 10.1109/ICIP.2016.7532566
M3 - Conference contribution
AN - SCOPUS:85006736322
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1289
EP - 1293
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -