Abstract
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 language | English |
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Title of host publication | Proceedings 2022 International Conference on Machine Learning Technologies |
Subtitle of host publication | ICMLT 2022 |
Publisher | ACM |
Pages | 141-146 |
Number of pages | 6 |
ISBN (Print) | 978-1-4503-9574-8 |
DOIs | |
Publication status | Published - 10 Jun 2022 |
Externally published | Yes |
Event | 7th International Conference on Machine Learning Technologies - Rome, Italy Duration: 11 Mar 2022 → 13 Mar 2022 |
Conference
Conference | 7th International Conference on Machine Learning Technologies |
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Abbreviated title | ICMLT 2022 |
Country/Territory | Italy |
City | Rome |
Period | 11/03/22 → 13/03/22 |
Keywords
- 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