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Hand Pose Regression via a Classification-Guided Approach

  • Hongwei Yang
  • Juyong ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

Hand pose estimation from single depth image has achieved great progress in recent years, however, up-to-data methods are still not satisfying the application requirements like in human-computer interaction. One possible reason is that existing methods try to learn a general regression function for all types of hand depth images. To handle this problem, we propose a novel “divide-and-conquer” method, which includes a classification step and a regression step. At first, a convolutional neural network classifier is used to classify the input hand depth image into different types. Then, an effective and efficient multiway cascaded random forest regressor is used to estimate the hand joints’ 3D positions. Experiments demonstrate that the proposed method achieves state-of-the-art performance on challenging dataset. Moreover, the proposed method can be easily combined with other regression method.

Keywords

Depth Image Convolutional Neural Network Hand Joint Threshold Interval Final Hand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

We thank Zishun Liu for his helpful suggestions on this paper. This work was supported by the National Key R&D Program of China (No. 2016YFC0800501), NSF of China (Nos. 61672481, 61303148), NSF of Anhui Province, China (No. 1408085QF119), Specialized Research Fund for the Doctoral Program of Higher Education under contract (No. 20133402120002).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of Science and Technology of ChinaHefeiChina

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