The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Hand gesture recognition with leap motion and kinect devices
Abstract
The recent introduction of novel acquisition devices like the Leap Motion and the Kinect allows to obtain a very informative description of the hand pose that can be exploited for accurate gesture recognition. This paper proposes a novel hand gesture recognition scheme explicitly targeted to Leap Motion data. An ad-hoc feature set based on the positions and orientation of the fingertips is computed and fed into a multi-class SVM classifier in order to recognize the performed gestures. A set of features is also extracted from the depth computed from the Kinect and combined with the Leap Motion ones in order to improve the recognition performance. Experimental results present a comparison between the accuracy that can be obtained from the two devices on a subset of the American Manual Alphabet and show how, by combining the two features sets, it is possible to achieve a very high accuracy in real-time.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Cited by
Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., & Kautz, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.456 · Cited by 695
A review of hand gesture and sign language recognition techniques
Cheok, M. J., Omar, Z., & Jaward, M. H. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-017-0705-5 · Cited by 600
Qi, W., Ovur, S. E., Li, Z., Marzullo, A., & Song, R. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3089999 · Cited by 253
Real-Time Hand Gesture Recognition Model Using Deep Learning Techniques and EMG Signals
Chung, E. A., & Benalcazar, M. E. (2019) · 2019 27th European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2019.8903136 · Cited by 47
Bardot, S., Serrano, M., & Jouffrais, C. (2016) · Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services · doi:10.1145/2935334.2935342 · Cited by 28
Metadata
- Authors
- Marin, G., Dominio, F., & Zanuttigh, P.
- Venue
- 2014 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2014-10
- Pages
- 1565-1569
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2052101136
- Cited by
- 442
- References
- 2
- FWCI
- 28.80
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hand gesture recognition with leap motion and kinect devices. https://app.cochat.ai/papers/10.1109/icip.2014.7025313
- The paper
- Marin, G., Dominio, F., & Zanuttigh, P. (2014). Hand gesture recognition with leap motion and kinect devices. 2014 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2014.7025313
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
