%0 Conference Paper %1 qiao2017principle %A Qiao, Yu %A Feng, Zhiquan %A Zhou, Xiaoyan %A Yang, Xiaohui %B 2017 2nd International Conference on Multimedia and Image Processing (ICMIP) %D 2017 %I IEEE %K analysis area-feature gesture-recognition human-computer-interaction machine-learning principal-component real %P 108-112 %R 10.1109/ICMIP.2017.36 %T Principle Component Analysis Based Hand Gesture Recognition for Android Phone Using Area Features %U https://ieeexplore.ieee.org/abstract/document/8221081 %X Hand gesture has been used in different applications and implemented on different platforms. Hence, a real-time and robust approach with high recognition accuracy is important in smart devices. This paper describes a novel method of hand gesture recognition using Principle Component Analysis (PCA) implemented in Android phone. Area features are adopted to do the gesture recognition. It solves these problems such as different size of gesture image captured, different angle of gesture's rotation and flipped gesture. Experiment results show that the average recognition rate of the proposed method is 93.95%. Moreover, the computation complexity of the proposed method is low, and it can be adopted in real-time applications.

@inproceedings{qiao2017principle, abstract = {Hand gesture has been used in different applications and implemented on different platforms. Hence, a real-time and robust approach with high recognition accuracy is important in smart devices. This paper describes a novel method of hand gesture recognition using Principle Component Analysis (PCA) implemented in Android phone. Area features are adopted to do the gesture recognition. It solves these problems such as different size of gesture image captured, different angle of gesture's rotation and flipped gesture. Experiment results show that the average recognition rate of the proposed method is 93.95%. Moreover, the computation complexity of the proposed method is low, and it can be adopted in real-time applications.}, added-at = {2019-11-14T06:23:47.000+0100}, author = {Qiao, Yu and Feng, Zhiquan and Zhou, Xiaoyan and Yang, Xiaohui}, biburl = {https://www.bibsonomy.org/bibtex/2ae83fe717d8c822d3e1ad3696f7bd8f9/jpmor}, booktitle = {2017 2nd International Conference on Multimedia and Image Processing (ICMIP)}, description = {Principle Component Analysis Based Hand Gesture Recognition for Android Phone Using Area Features - IEEE Conference Publication}, doi = {10.1109/ICMIP.2017.36}, interhash = {d108fda335b7c2b4f6f6916e732283cb}, intrahash = {ae83fe717d8c822d3e1ad3696f7bd8f9}, keywords = {analysis area-feature gesture-recognition human-computer-interaction machine-learning principal-component real}, month = {March}, pages = {108-112}, publisher = {IEEE}, school = {University of Jinan (UJN)}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Principle Component Analysis Based Hand Gesture Recognition for Android Phone Using Area Features}, url = {https://ieeexplore.ieee.org/abstract/document/8221081}, year = 2017 }