Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 246-249.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Gesture Recognition Based on Hand Geometric Distribution Feature

HAN Xiao, ZHANG Jing, LI Yue-long   

  1. School of Computer Science and Software,Tianjin Polytechnic University,Tianjin 300387,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: Aiming at the problem that gestures are affected by scaling and rotation,resulting in low recognition rate,this paper proposed a feature extraction method based on hand geometric distribution for gesture recognition.Firstly,the segmented gesture image is normalized.Secondly,width-to-length ratio of the minimum circumscribed rectangle of gesture main direction and gesture contour is calculated,and the similarity function is used as preliminary recognition to select some candidate gestures.Finally,contour segmentation method is used to estimate the distribution of gesture contour points in polar coordinates and the modified Hausdorff distance is used as a similarity measure method to identify the final gesture.The experimental results show that the proposed method can identify various gestures quickly and accurately,the average recognition rate reaches 92.89%,the false recognition rate is reduced to 3.53%,and the recognition speed is 4.2 times higher than that of similar algorithms.

Key words: Gesture main direction, Geometric distribution, Feature extraction, Contour segmentation, Modified Hausdorff distance, Gesture recognition

CLC Number: 

  • TP391
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