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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



HAND POSES DETECTION USING COVOLUTIONAL NEURAL NETWORK

[Full Text]

 

AUTHOR(S)

Md. Buran Basha, S. Ravi Teja, K. Pavan Kumar, M.V.S.D. Anudeep

 

KEYWORDS

Convolutional Neural Network, Human computer interaction, Gesture recognition, Deep learning, Hand gesture

 

ABSTRACT

Hand gesture recognition is the method by which an individual involving only hands understands specific forms of shape and movement. There are many applications where it is possible to apply hand gesture recognition to enhance control, usability, interaction and training. Communication through hand gesture has been shown successful results for humans and active research continues to replicate the same performance in computer vision systems. Interaction between humans and computers can be significantly enhanced by developments in systems capable of recognizing different hand movements. In this paper we have considered leapGestRecog data set which consists of 1000 images of 10 different members and each of them constituting 9 different labels of hand i.e palm, fist, thumb, index, ok, c, down, palm moved, fist moved. We are using convolutional neural networks which provide a very good result when dealing with images. This hand gesture recognition can be widely in case of physically impaired people and video games which involve gestures to move or play. Now-a- days by showing some gestures we can open some applications in mobile phone. The main goal of hand poses detection is to detect the gesture and able to control it.

 

REFERENCES

[1] WEI FANG et.al., proposed “Gesture recognition based on CNN & DCGAN for calculation and text output” IEEE Transactions on 10.1109/ACCESS 2019.2901930.
[2] CHRISTAIN WOLF et.al., proposed “Multi-Scale Deep Learning for Gesture Detection and Localization” ECCV Workshop pp 474-490 on 19 March 2015.
[3] M.Stecher et.al., proposed “Tracking down the illutiveness of gesture interaction in the truck domain” 6th International Conference on AHFE 3(2015) 3176-3183.
[4] Jawad Negi et.al., proposed “Max pooling convolutional neural networks foe vision-based hand gesture recognition” International Conference on Signal and Image Processing Applications 2011.
[5] Vijay John et.al., proposed “Deep Learning-Based Fast Hand Gesture Recognition Using Representative Frames” International Conference on digital image computing: Techniques and Applications 2016.
[6] Carl A. Pickering et.al., proposed “A reasearch study of hand gesture recognition technologies and applications for huamn vehicle interaction” 3rd Institute of Engineering and Technology Conference on Automative Electronics 2007.
[7] Soeb Hussain et.al.,proposed”Hand Gesture Recognition using deep learning” International SoC Design Conference 2017.
[8] Adnan Khashman et.al., proposed “Deep Learning in vision-based static hand gesture” Neural Computing and Applications Volume 28 pp 3944-3951 2016.
[9] S.Marcel et.al., proposed “Hand Gesture Recognition using input-output hidden markov models” Fourth IEEE International Conference on Automatic Face and Gesture Recognition PR00580 2000.
[10] Hatice Gunes et.al., proposed “Face and Body Gesture Recognition for a Vision-Based Multimodal Analyzer Computer Research Group PO 123 2007.
[11] Simonyan, K., Zisserman, A.: Two-Stream Convolutional Networks for Action Recognition in Videos. In: arXiv preprint arXiv:1406.2199v1 (2014)
[12] Bilal, S., Akmeliawati, R., El Salami, M.J., Shafie, A.A.: Vision-based hand posture detection and recognition for sign languagea study. In: 2011 4th International Conference on Mechatronics (ICOM), pp. 1–6. IEEE (2011)
[13] Molchanov, P., Gupta, S., Kim, K., Kautz, J.: Hand gesture recognition with 3d convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–7 (2015)
[14] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
[15] ) Rafiqul Zaman Khan et.al., proposed “Comparative Study of Hand Gesture Recognition System” ACSIT DOI:10.5121/csit.2012.2320 2012.