Computer Science ›› 2020, Vol. 47 ›› Issue (7): 103-110.doi: 10.11896/jsjkx.200100073

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Hand Gesture Recognition Based on Self-adaptive Multi-classifiers Fusion

LIU Xiao1, YUAN Guan1,2, ZHANG Yan-mei1, YAN Qiu-yan1, WANG Zhi-xiao1   

  1. 1 College of Computer Science and Technology,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China
    2 Digitization of Mine,Engineering Research Center of Ministry of Education,Xuzhou,Jiangsu 221116,China
  • Received:2020-01-13 Online:2020-07-15 Published:2020-07-16
  • About author:LIU Xiao,born in 1994,postgraduate.His main research interests include data mining,pattern recognition and perceptual computing.
    YUAN GUAN,born in 1982,Ph.D,professor,is a member of China Computer Federation.His main research interests include artificial intelligence,big data technology,machine learning and computational intelligence.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (71774159,61876186,61977061),China Postdoctoral Science Foundation(2018M642358) and Think Tank of Green Safety Management and Policy Science (2018WHCC03)

Abstract: In order to improve the performance of hand gesture recognition based on wearable devices,a hand gesture recognition method (SAMCF) based on self-adaptive multi-classifiers fusion is proposed to solve the bias of single classifier in hand gesture recognition.First,for statistical features that cannot characterize intra-class variability and similarity between complex gestures,SAMCF uses a convolutional neural network (CNN) to automatically extract depth features with strong representation capabilities.Then,SAMCF uses multiple basic classifiers to recognize the extracted feature vectors,and determines the optimal recognition result through self-adaptive fusion algorithm,which solves the bias of single classifier.After that,the robustness and genera-lization ability of the model are verified by using the data set collected by data glove.The experimental results show that SAMCF can effectively extract the depth features of gesture,solve the bias of single classifier,and improve the efficiency of hand gesture recognition and enhance the performance of hand gesture recognition.The recognition accuracy of character level hand gesture (American Sign Language and Arabic numerals) is 98.23%,which is 5% higher than other algorithms on average;the recognition accuracy of word level gesture (Chinese Sign Language) is 97.81%,which is 4% higher than other algorithm on average.

Key words: Convolutional neural network, Data glove, Hand gesture recognition, Multi-classifiers, Self-adaptive fusion algorithm

CLC Number: 

  • TP391
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