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The Study of Gesture Recognition Based on SVM with LBP and PCA

Jingzhong Wang 1, Xiaoqing Xu 1,2, and Meng Li 1,2
1. North China University of Technology, Beijing, China
2. Beijing Electronic Science and Technology Institute Beijing, Beijing, China

Abstract—This paper gives an improved gesture recognition algorithm. First, obtain the complete hand-type region of the image, using the image preprocess algorithm such as YUV color segmentation, image differencing, connected domain detection. Then process images through contour detection, have the feature extraction and compression by LBP transform and principal component analysis. Finally, use support vector machine as a training machine learning algorithms to build classifier classification. To research a total of 630 gesture images, the experimental results show that the proposed method for gesture recognition, which can improve the recognition rate and speed effectively, and the recognition rate reaches 94.22%.

Index Terms—gesture recognition, LBP, PCA, SVM, machine learning

Cite: Jingzhong Wang, Xiaoqing Xu, and Meng Li, "The Study of Gesture Recognition Based on SVM with LBP and PCA," Journal of Image and Graphics, Vol. 3, No. 1, pp. 16-19, June 2015. doi: 10.18178/joig.3.1.16-19