期刊
PATTERN RECOGNITION
卷 71, 期 -, 页码 132-143出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2017.06.009
关键词
Convolutional Neural Networks; Head pose estimation; Adaptive gradient; Deep learning
资金
- Air Force Office of Scientific Research [A9550-15-1-0025]
- Air Force Materiel Command, USAF [FA9550-15-10025]
- NVIDIA Corporation
Head pose estimation is an old problem that is recently receiving new attention because of possible applications in human-robot interaction, augmented reality and driving assistance. However, most of the existing work has been tested in controlled environments and is not robust enough for real-world applications. In order to handle these limitations we propose an approach based on Convolutional Neural Networks (CNNs) supplemented with the most recent techniques adopted from the deep learning community. We evaluate the performance of four architectures on recently released in-the-wild datasets. Moreover, we investigate the use of dropout and adaptive gradient methods giving a contribution to their ongoing validation. The results show that joining CNNs and adaptive gradient methods leads to the state-of-the-art in unconstrained head pose estimation. (C) 2017 Elsevier Ltd. All rights reserved.
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