TY - GEN
T1 - TinyPoseNet
T2 - 24th International Conference on Neural Information Processing, ICONIP 2017
AU - Li, Shanru
AU - Wang, Liping
AU - Yang, Shuang
AU - Wang, Yuanquan
AU - Wang, Chongwen
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - As an inherent attribute of human, head pose plays an important role in many tasks. In this paper, we formulate head pose estimation in different directions as a multi-task regression problem, and propose a fast, compact and robust head pose estimation model, named TinyPoseNet. Specifically, we combine the tasks of head pose estimation in different directions into one joint learning task and design the whole model based on the principle of “being deeper” and “being thinner” to obtain a tiny model with specially designed types and particular small numbers of filters. We perform thorough experiments on 3 types of test sets and compare our method with others from several different aspects, including the accuracy, the speed, the compactness and so on. In addition, we introduce large angle data in Multi-PIE to verify the ability of dealing with large-scale pose in practice. All the experiments demonstrate the advantages of the proposed model.
AB - As an inherent attribute of human, head pose plays an important role in many tasks. In this paper, we formulate head pose estimation in different directions as a multi-task regression problem, and propose a fast, compact and robust head pose estimation model, named TinyPoseNet. Specifically, we combine the tasks of head pose estimation in different directions into one joint learning task and design the whole model based on the principle of “being deeper” and “being thinner” to obtain a tiny model with specially designed types and particular small numbers of filters. We perform thorough experiments on 3 types of test sets and compare our method with others from several different aspects, including the accuracy, the speed, the compactness and so on. In addition, we introduce large angle data in Multi-PIE to verify the ability of dealing with large-scale pose in practice. All the experiments demonstrate the advantages of the proposed model.
KW - Data augmentation
KW - Deep learning
KW - Head pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85035083690&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70096-0_6
DO - 10.1007/978-3-319-70096-0_6
M3 - Conference contribution
AN - SCOPUS:85035083690
SN - 9783319700953
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 63
BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
A2 - Zhao, Dongbin
A2 - El-Alfy, El-Sayed M.
A2 - Liu, Derong
A2 - Xie, Shengli
A2 - Li, Yuanqing
PB - Springer Verlag
Y2 - 14 November 2017 through 18 November 2017
ER -