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A multi-branch hand pose estimation network with joint-wise feature extraction and fusion
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Indexed by:Journal Papers

First Author:Li, Xuefeng

Correspondence Author:Sun, Y (reprint author), Dalian Univ Technol, Dalian, Peoples R China.

Co-author:Zhou, Yidan,Sun, Yi,Lin, Xiangbo,Ma, Xiaohong

Date of Publication:2020-02-01

Journal:SIGNAL PROCESSING-IMAGE COMMUNICATION

Included Journals:EI、SCIE

Volume:81

ISSN No.:0923-5965

Key Words:Hand pose estimation; Neural network; Human-computer interaction; Depth images

Abstract:The study of 3D hand pose estimation from a single depth image is regarded as a detection-based or regression-based problem among most of the existing deep learning-based methods, and this approach does not fully exploit the geometry of the hand, such as its structural and physical constraints. To overcome these weaknesses, we design a network with three simple parallel branches that correspond to the three functional parts of the hand. This observation is motivated by the biological viewpoint that each finger plays a different role in performing grasping and manipulation. In each branch, we perform a more detailed regression in two stages - top-down joint location regression followed by bottom-up hand pose regression - which fully exploits both the local and global structure of a hand. Finally, we further make use of the hand structure and physical constraints to refine each joint by its auxiliary points. The proposed network is a unified structure and function model that is more appropriate for hand pose estimation. Our system does not require pose pre-processing or feedback since it can directly perform training and predicting from end-to-end. The experimental results on three public datasets demonstrate that the proposed system achieves performance comparable to state-of-the-art methods.

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Gender:Female

Alma Mater:大连理工大学

Degree:Doctoral Degree

School/Department:信息与通信工程学院

Business Address:海山楼A420

Contact Information:lslwf@dlut.edu.cn

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