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MFDNet: Collaborative Poses Perception and Matrix Fisher Distribution for Head Pose Estimation

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成果类型:
期刊论文
作者:
Liu, Hai;Fang, Shuai;Zhang, Zhaoli;Li, Duantengchuan;Lin, Ke;...
通讯作者:
Fang, S.
作者机构:
[Li, Duantengchuan; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
[Fang, Shuai] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
[Lin, Ke] Harbin Inst Technol, Control Sci & Engn, Shenzhen 150001, Peoples R China.
[Wang, Jiazhang] Northwestern Univ, Evanston, IL 60208 USA.
通讯机构:
[Fang, S.] C
Central China Normal University, National Engineering Laboratory For Educational Big Data, Wuhan, China
语种:
英文
关键词:
Head;Pose estimation;Three-dimensional displays;Measurement;Feature extraction;Uncertainty;Solid modeling;Head pose estimation;Triplet loss;Rotation matrix;Matrix Fisher distribution;Metric learning
期刊:
IEEE Transactions on Multimedia
ISSN:
1520-9210
年:
2022
卷:
24
页码:
2449-2460
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62011530436, 62077020, 62005092 and 61875068) 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: CCNU20ZT017 and CCNU2020ZN008)
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Head pose estimation suffers from several problems, including low pose tolerance under different disturbances and ambiguity arising from common head pose representation. In this study, a robust three-branch model with triplet module and matrix Fisher distribution module is proposed to address these problems. Based on metric learning, the triplet module employs triplet architecture and triplet loss. It is implemented to maximize the distance between embeddings with different pose pairs and minimize the distance between embeddings with same pose ...

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