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Real Time Palm and Finger Detection for Gesture Recognition Using Convolution Neural Network

  • R. RamyaEmail author
  • K. Srinivasan
Chapter
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 6)

Abstract

Hand motion is one of the methods for communicating with the PC to perform colossal application. The goal is to apply the machine learning algorithm for quicker motion route acknowledgment. This application pursues the learning of motions and recognizing them accurately. At first Convolution Neural Network (CNN) model is prepared with various pictures of various hand signals for different people for grouping the letters in order. The initial step is realizing, where the data is stacked and in pre-handling step, edge, mass locators, twofold thresholding, limit box recognitions are used to extract the features. By utilizing ConvNet, which is a machine learning algorithm, the input picture’s features are cross confirmed. The accuracy is found to be 98% and this methodology is effective to address the impact of various problems. The assessment of this strategy shows to performing hand gesture acknowledgment.

Keywords

Image processing Convolution Neural Network Deep Learning Gesture identification 

References

  1. 1.
    S. Shaikh, R. Gupta, I. Shaikh, J. Borade, Hand gesture recognition using OpenCV. Int. J. Adv. Res. Comput. Commun. Eng. 5(3), 2275–2321 (2016)Google Scholar
  2. 2.
    M. Panwar, P.S. Mehra, Hand gesture recognition for human computer interaction, in IEEE International Conference on Image Information Processing (ICIIP 2011), Waknaghat, India, Nov 2011Google Scholar
  3. 3.
    D.K. Ray, M. Soni, P. Johri, A. Gupta, Hand gesture recognition using python. Int. J. Future Revolut. Comput. Sci. Commun. Eng. 4(6), 2459–5462 (2004) Google Scholar
  4. 4.
    A. Chitade, S. Katiyar, Color based image segmentation using K-means clustering. Int. J. Eng. Sci. Technol. 2(10), 5319–5325 (2010)Google Scholar
  5. 5.
    Y. Guan, M. Zheng, Real-time 3D pointing gesture recognition for natural HCI, in Proceedings of the World Congress on Intelligent Control and Automation, China, 2008, pp. 2433–2436Google Scholar
  6. 6.
    E. Stergiopoulou, N. Papamarkos, A new technique for hand gesture recognition, in IEEE—ICIP, 2006, pp. 2657–2660Google Scholar
  7. 7.
    C. Herbon, K. Toninies, B. Stock, Detection and segmentation of clustered objects by using iterative classification, segmentation, and Gaussian mixture models and applications to wood log detection, in German Conference on Pattern Recognition (Springer International Publishing, 2014), pp. 354–364Google Scholar
  8. 8.
    D. Kumar, K. Kumar, Review on different techniques of image segmentation using MATLAB. Int. J. Sci. Eng. Technol. 5(2) (2017)Google Scholar
  9. 9.
    B.N. Subudhi, I. Patwa, A. Ghosh, S.-B. Cho, Edge preserving region growing for aerial color image segmentation, in Intelligent Computing, Communication and Devices (Springer India, 2015), pp. 481–488Google Scholar
  10. 10.
    M. Panwar, Implementation of hand gesture recognition based on shape parameters, in IEEE International Conference on Image Information Processing, Dindigul, Tamil Nadu, India, Feb 2012Google Scholar
  11. 11.
    H. Renuka, B. Goutam, Hand gesture recognition system to control soft front panels. Int. J. Eng. Res. Technol. (2014)Google Scholar
  12. 12.
    J. Liu, W. Gui, Q. Chen, Z. Tang, C. Yang,“An unsupervised method for flotation froth image segmentation evaluation base on image gray-level distribution, in 23rd Chinese Control Conference (IEEE, 2013), pp. 4018–4022Google Scholar
  13. 13.
    A. Jinda-Apiraksa, W. Pongstiensak, T. Kondo, A simple shape based approach to hand gesture recognition, in IEEE International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), Pathum Thani, Thailand, May 2010, pp. 851–855Google Scholar
  14. 14.
    A.B. Jmaa, W. Mahdi, A new approach for digit recognition based on hand gesture analysis. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 2(2) (2009)Google Scholar
  15. 15.
    A. Sepehri, Y. Yacoob, L. Davis, Employing the hand as an interface device. J. Multimedia 1(7), 18–29 (2006)Google Scholar
  16. 16.
    S. Phung, A. Bouzerdoum, D. Chai, Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)CrossRefGoogle Scholar
  17. 17.
    Z.Y. He, A new feature fusion method for handwritten character recognition based on 3D accelerometer. Front. Manuf. Des. Sci. 44, 1583–1587 (2011)CrossRefGoogle Scholar
  18. 18.
    Y. Zhao, C. Lian, X. Zhang, X. Sha, G. Shi, W.J. Li, Wireless IoT motion-recognition rings and a paper keyboard. IEEE Access 7, 44514–44524 (2019)CrossRefGoogle Scholar
  19. 19.
    T.H. Lee, H.J. Lee, Ambidextrous virtual keyboard design with finger gesture recognition, in Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), May 2018, pp. 1–4Google Scholar
  20. 20.
    Y. Zhang, W. Yan, A. Narayanan, A virtual keyboard implementation based on finger recognition, in Proceedings of International Conference on Image and Vision Computing New Zealand (IVCNZ), Dec 2017, pp. 1–6Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of EIESri Ramakrishna Engineering CollegeCoimbatoreIndia

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