The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Survey on 3D Hand Gesture Recognition
Abstract
Three-dimensional hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 1) 3D hand modeling; 2) static hand gesture recognition; 3) hand trajectory gesture recognition; and 4) continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Analysis of the Accuracy and Robustness of the Leap Motion Controller
Weichert, F., Bachmann, D., Rudak, B., & Fisseler, D. (2013) · Sensors · doi:10.3390/s130506380 · Cited by 913
Real time hand pose estimation using depth sensors
Keskin, C., Kirac, F., Kara, Y. E., & Akarun, L. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130391 · Cited by 190
Cited by
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces
Kim, M., Cho, J., Lee, S., & Jung, Y. (2019) · Sensors · doi:10.3390/s19183827 · Cited by 169
Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks
Ketyko, I., Kovacs, F., & Varga, K. Z. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852018 · Cited by 94
Hachaj, T., & Piekarczyk, M. (2019) · Sensors · doi:10.3390/s19245408 · Cited by 30
Securing Insulin Pump System Using Deep Learning and Gesture Recognition
Ahmad, U., Song, H., Bilal, A., Saleem, S., & Ullah, A. (2018) · 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) · doi:10.1109/trustcom/bigdatase.2018.00258 · Cited by 23
Metadata
- Authors
- Cheng, H., Yang, L., & Liu, Z.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2016-09
- Volume
- 26
- Issue
- 9
- Pages
- 1659-1673
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2511561981
- Cited by
- 344
- References
- 3
- FWCI
- 58.48
- Type
- journal article
- Language
- en
- Open access
- closed
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