A Deep Learning Approach to Automatically Extract 3D Hand Measurements

Nastaran Kaashki, Xinxin Dai, Timea Gyarmathy, Pengpeng Hu, Bogdan Iancu, Adrian Munteanu

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


Accurate hand measurement data is of crucial importance in medical science, fashion industry, and augmented/virtual reality applications. Conventional methods extract the hand measurements manually using a measuring tape, thereby being very time-consuming and yielding unreliable measurements. In this paper, we propose–to the best of our knowledge–the first deep-learning-based method to automatically measure the hand in a non-contact manner from a single 3D hand scan. The proposed method employs a 3D hand scan, extracts the features, reconstructs the hand by making use of a 3D hand template, transfers the measurements defined on the template and extracts them from the reconstructed hand. In order to train, validate, and test the method, a novel large-scale synthetic hand dataset is generated. The results on both the unseen synthetic data and the unseen real scans captured by the Occipital structure sensor Mark I demonstrate that the proposed method outperforms the state-of-the-art method in most hand measurement types.
Original languageEnglish
Title of host publicationProceedings 2022 International Conference on Machine Learning Technologies
Subtitle of host publicationICMLT 2022
Number of pages6
ISBN (Print)978-1-4503-9574-8
Publication statusPublished - 10 Jun 2022
Externally publishedYes
Event7th International Conference on Machine Learning Technologies - Rome, Italy
Duration: 11 Mar 202213 Mar 2022


Conference7th International Conference on Machine Learning Technologies
Abbreviated titleICMLT 2022


  • Hand measurement extraction
  • deep neural networks
  • synthetic data
  • template fitting

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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