Preliminary Study on Hand Gesture Recognition Based on Convolutional Neural Network
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [11]
  • |
  • Related [20]
  • |
  • Cited by [0]
  • | |
  • Comments
    Abstract:

    The paper proposed a new algorithm used for hand gesture recognition which based on the convolutional neural network. The method not only avoids the hand gesture in the early period of the complex pretreatment, but also can directly input the gesture of original image. The convolutional neural network is characterized by local receptive field, hierarchical structure, global learning for feature extraction and classical. It has been applied to many image recognition tasks. Experimental results showed that the multi-class hand gestures can be recognized with high accuracy, small complexity and good robustness, while the inherent shortcomings of the traditional algorithm are overcame.

    Reference
    1 张凯.基于立体视觉的自然手势识别[学位论文].北京:北京大学,2005.
    2 殷涛.基于几何矩的手势识别算法[学位论文].上海:上海海运学院,2004.
    3 LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. USA: IEEE, 1998: 2278-2324.
    4 Lauer F, Suen CY, Bloch G. A trainable feature extractor for handwritten digit recognition. Pattern Recognition, 2007, 40(6):1816-1824.
    5 Lawrence S, Giles CL, Tsoi AC, Back AD. Face recognition: A convolutional neural network approach. IEEE Trans. on Neural Networks, 1997, 8(1): 98-113.
    6 Tivive FHC, Bouzerdoum A. An eye feature detector based on convolutional neural network. Proc. 8th Int. Symp. Signal Process. Applic. Sydney, New South Wales, Australia. IEEE, 2005: 90-93.
    7 Mate S, Akira Y, Munetaka Y, Jun O. Pedestrian detection with convolutional neural networks. IEEE Intelligent Vehicles Symposium Proceedings. USA: IEEE, 2005: 224-229.
    8 Cun YL, Muller U, Ben J, Cosatto E, Flepp B. Off-road obstacle avoidance through end-to-end learning. Advances in Neural Information Processing Systems. USA: MIT Press, 2005.
    9 赵志宏,杨绍普,马增强.基于卷积神经网络LeNet-5的车牌字符识别研究.系统仿真学报,2010,22(3):638-641.
    10 徐姗姗,刘应安,徐昇.基于卷积圣经网络的木材缺陷识别.山东大学学报(工学版),2013,43(2):23-28.
    11 许可.卷积神经网络在图像识别上的应用研究[学位论文].杭州:浙江大学,2012.
    Comments
    Comments
    分享到微博
    Submit
Get Citation

蔡娟,蔡坚勇,廖晓东,黄海涛,丁侨俊.基于卷积神经网络的手势识别初探.计算机系统应用,2015,24(4):113-117

Copy
Share
Article Metrics
  • Abstract:2366
  • PDF: 6123
  • HTML: 0
  • Cited by: 0
History
  • Received:July 28,2014
  • Revised:September 29,2014
  • Online: April 24,2015
Article QR Code
You are the first990466Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063