3D head pose estimation with convolutional neural network trained on synthetic images

Xiabing Liu, Wei Liang, Yumeng Wang, Shuyang Li, Mingtao Pei

科研成果: 书/报告/会议事项章节会议稿件同行评审

86 引用 (Scopus)

摘要

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.

源语言英语
主期刊名2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
出版商IEEE Computer Society
1289-1293
页数5
ISBN(电子版)9781467399616
DOI
出版状态已出版 - 3 8月 2016
活动23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, 美国
期限: 25 9月 201628 9月 2016

出版系列

姓名Proceedings - International Conference on Image Processing, ICIP
2016-August
ISSN(印刷版)1522-4880

会议

会议23rd IEEE International Conference on Image Processing, ICIP 2016
国家/地区美国
Phoenix
时期25/09/1628/09/16

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