Accurate Hand Keypoint Localization on Mobile Devices

Brief description

We present a novel approach for 2D hand keypoint localization from regular color input. The proposed approach relies on an appropriately designed Convolutional Neural Network (CNN) that computes a set of heatmaps, one per hand keypoint of interest. Extensive experiments with the proposed method compare it against state of the art approaches and demonstrate its accuracy and computational performance on standard, publicly available datasets. The obtained results demonstrate that the proposed method matches or outperforms the competing methods in accuracy, but clearly outperforms them in computational efficiency, making it a suitable building block for applications that require hand keypoint estimation on mobile devices.

Sample results

Video with experimental results

Download library

Download from Github repository.


  • Filippos Gouidis, Paschalis Panteleris, Iason Oikonomidis, Alexandros Makris, Antonis A. Argyros
  • This work was partially supported by the EU and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE- INNOVATE (project code:T1EDK-01299 - HealthSign). Also co-financed by the H2020-ICT-2016-1-731869 project Co4Robots.

Relevant publications

  • F. Gouidis, P. Panteleris, I. Oikonomidis, A.A. Argyros, “Accurate Hand Keypoint Localization on Mobile Devices”, Machine Vision Applications (MVA 2019), Tokyo, Japan, May, 2019.

The electronic versions of the above publications can be downloaded from my publications page.