Ivo Aluízio Stinghen Filho
ABSTRACT
In this paper we compare the effectiveness of various methods of machine learning algorithms for real-time hand gesture recognition, in order to find the most optimal way to identify static hand gestures, as well as the most optimal sample size for use during the training step of the algorithms.
In our framework, Leap Motion and Unity were used to extract the data. The data was then used to be trained using Python and scikit-learn. Utilizing normalized information regarding the hands and fingers, we managed to get a hit rate of 97% using the decision tree classifier.
Supplemental Material
- Feiyu Chen, Jia Deng, Zhibo Pang, Majid Baghaei Nejad, Huayong Yang, and Geng Yang. 2018. Finger Angle-Based Hand Gesture Recognition for Smart Infrastructure Using Wearable Wrist-Worn Camera. Applied Sciences 8, 3 (2018), 369.Google Scholar
Cross Ref
- Steve R Gunn et al. 1998. Support vector machines for classification and regression. ISIS technical report 14, 1 (1998), 5--16.Google Scholar
- Ramos Ribeiro et al. 2016. Framework for registration and recognition of free-hand gestures in digital games. SBGames.Google Scholar
- Lin Shao. 2016. Hand movement and gesture recognition using Leap Motion Controller. Stanford University, Stanford, CA.Google Scholar
- Dengfeng Yao, Minghu Jiang, Abudoukelimu Abulizi, and Xu You. 2014. Decision-tree-based algorithm for 3D sign classification. In Signal Processing (ICSP), 2014 12th International Conference on. IEEE, 1200--1204.Google ScholarCross Ref
Index Terms
Gesture recognition using leap motion: a comparison between machine learning algorithms
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