Nastaran Nourbakhsh Kaashki
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.
- BJ Dunbar and PJ Chapates. 2019. Comparison of 3D photogrammetric and laser hand scans to manual measurement methods for EVA glove fabrication. In IEEE Aerospace Conference. 1–11.Google Scholar
Cross Ref
- Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. 2018. 3D-CODED: 3D correspondences by deep deformation. In European Conference on Computer Vision (ECCV). 230–246.Google ScholarDigital Library
- Hyun Sook Han and Chang Kyu Park. 2016. Automatic Hand Measurement System from 2D Hand Image for Customized Glove Production. Fashion & Textile Research Journal 18, 4 (2016), 468–476.Google ScholarCross Ref
- Karim Rezwan Hasan, Shamim Ara, and Fakhrul Amin Mohammad Hasanul Banna. 2017. Correlation of Index Finger Length (2D) with height, weight and BMI in adult Bangladeshi male. Journal of Enam Medical College 7, 2 (2017), 90–94.Google ScholarCross Ref
- Pengpeng Hu, Nastaran Nourbakhsh Kaashki, Vasile Dadarlat, and Adrian Munteanu. 2020. Learning to estimate the body shape under clothing from a single 3-D scan. IEEE Transactions on Industrial Informatics 17, 6 (2020), 3793–3802.Google ScholarCross Ref
- Sultana Monira Hussain, Yuanyuan Wang, David C Muller, Anita E Wluka, Graham G Giles, John T Manning, Stephen Graves, and Flavia M Cicuttini. 2014. Association between index-to-ring finger length ratio and risk of severe knee and hip osteoarthritis requiring total joint replacement. Rheumatology 53, 7 (2014), 1200–1207.Google ScholarCross Ref
- Nastaran Nourbakhsh Kaashki, Pengpeng Hu, and Adrian Munteanu. 2021. Anet: A Deep Neural Network for Automatic 3D Anthropometric Measurement Extraction. IEEE Transactions on Multimedia(2021). early access.Google Scholar
- Nastaran Nourbakhsh Kaashki, Pengpeng Hu, and Adrian Munteanu. 2021. Deep Learning-Based Automated Extraction of Anthropometric Measurements From a Single 3-D Scan. IEEE Transactions on Instrumentation and Measurement 70 (2021), 1–14.Google ScholarCross Ref
- Nastaran Nourbakhsh Kaashki and Reza Safabakhsh. 2014. 3D constrained local model-based face recognition using Kinect under variant conditions. In International Symposium on Telecommunications (IST’2014). 361–366.Google ScholarCross Ref
- Nastaran Nourbakhsh Kaashki and Reza Safabakhsh. 2018. RGB-D face recognition under various conditions via 3D constrained local model. Journal of Visual Communication and Image Representation 52 (2018), 66–85.Google ScholarDigital Library
- Chia-Chen Kuo, Hui-Yi Kung, Hsin-Chieh Wu, and Mao-Jiun Wang. 2020. Developing a hand sizing system for a hand exoskeleton device based on the Kansei Engineering method. Journal of Ambient Intelligence and Humanized Computing (2020), 1–13.Google Scholar
- Mei-ying Kwan, Kit-lun Yick, Lung Chow, Annie Yu, Sun-pui Ng, and Joanne Yip. 2021. Impact of postural variation on hand measurements: Three-dimensional anatomical analysis. PloS one 16, 4 (2021).Google Scholar
- Dukhum Magu, Aditya Aggarwal, Prateek Behera, and Ankit Khurana. 2020. Use of finger length ratio as a marker for knee osteoarthritis: A case-control study of 2,456 patients. medRxiv (2020).Google Scholar
- Lukasz Markiewicz, Marcin Witkowski, Robert Sitnik, and Elzbieta Mielicka. 2017. 3D anthropometric algorithms for the estimation of measurements required for specialized garment design. Expert Systems with Applications 85 (2017). https://doi.org/10.1016/j.eswa.2017.04.052Google ScholarDigital Library
- Fionn Murtagh. 1991. Multilayer perceptrons for classification and regression. Neurocomputing 2, 5-6 (1991), 183–197.Google ScholarCross Ref
- Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, and Michael J. Black. 2019. Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
- Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 652–660.Google Scholar
- Charles R Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. arXiv preprint arXiv:1706.02413(2017).Google Scholar
- Emily Seifert and Linsey Griffin. 2020. Comparison and Validation of Traditional and 3D Scanning Anthropometric Methods to Measure the Hand. In Int. Conference and Exhibition on 3D Body Scanning and Processing Technologies.Google ScholarCross Ref
- Chengde Wan, Thomas Probst, Luc Van Gool, and Angela Yao. 2020. Dual grid net: Hand mesh vertex regression from single depth maps. In European Conference on Computer Vision. 442–459.Google ScholarDigital Library
- Wei-Wen Wang, Kai-Wen Lee, Sheng-Yen Lin, Chia-Hsun Lin, Li-Chen Fu, Jin-Shin Lai, Jer-Junn Luh, Wen-Shiang Chen, and Tyng-Guey Wang. 2013. A joint localizer for finger length measurements. In IEEE International Symposium on Medical Measurements and Applications (MeMeA). 111–115.Google ScholarCross Ref
- Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. 2018. Dynamic graph CNN for learning on point clouds. arXiv preprint arXiv:1801.07829(2018).Google Scholar
Index Terms
- A Deep Learning Approach to Automatically Extract 3D Hand Measurements
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