Human Computer Interaction – Hand Gesture Recognition

Riya Jain; Muskan Jain; Roopal Jain; Suman Madan

doi:10.21467/ajgr.11.1.1-9

Authors

Riya Jain Jagan Institute of Management Studies, Sec-5, Rohini, Delhi
Muskan Jain Jagan Institute of Management Studies, Sec-5, Rohini, Delhi https://orcid.org/0000-0001-5030-9258
Roopal Jain Jagan Institute of Management Studies, Sec-5, Rohini, Delhi https://orcid.org/0000-0002-6861-5066
Suman Madan Jagan Institute of Management Studies, Sec-5, Rohini, Delhi https://orcid.org/0000-0001-8145-9304

DOI:

https://doi.org/10.21467/ajgr.11.1.1-9

Abstract

The creation of intelligent and natural interfaces between users and computer systems has received a lot of attention. Several modes of knowledge like visual, audio, and pen can be used individually or in combination have been proposed in support of this endeavour. Human communication relies heavily on the use of gestures to communicate information. Gesture recognition is a subject of science and language innovation that focuses on numerically quantifying human gestures. It is possible for people to communicate properly with machines using gesture recognition without the use of any mechanical devices. Hand gestures are a form of nonverbal communication that can be applied to several fields, including deaf-mute communication, robot control, human–computer interaction (HCI), home automation, and medical applications. Many different methods have been used in hand gesture research papers, including those focused on instrumented sensor technology and computer vision. To put it another way, the hand sign may be categorized under a variety of headings, including stance and motion, dynamic and static, or a combination of the two. This paper provides an extensive study on hand gesture methods and explores their applications.

Keywords:

Hand gesture, Computer Vision, Human–computer interaction

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References

Munir Oudah,; Ali Al-Naji.; Javaan Chahl.; “Hand gesture recognition based on computer vision: A review on technique” Journal of Imaging, 6 , 73, 2020.

Perimal, M.; Basah, S.N.; Safar, M.J.A.; Yazid, “Hand-Gesture Recognition-Algorithm based on Finger Counting”, J. Telecommun. Electron. Comput. Eng.,10, 19–24, 2018.

Sulyman, A.B.D.A.; Sharef, Z.T.; Faraj, K.H.A.; Aljawaryy, Z.A.; Malallah, F.L. , “Real-time numerical 0-5 counting based on hand-finger gestures recognition.”, J. Theor. Appl. Inf. Technol. 2017, 95, 3105–3115.

Desai, S.; Desai, A., “Human Computer Interaction through hand gestures for home automation using Microsoft Kinect.” In Proceedings of the International Conference on Communication and Networks, Xi’an, China, pp. 19–29,10–12 October 2017.

Prakash, J.; Gautam, U.K. , “Hand Gesture Recognition.”, Int. J. Recent Technol. Eng. 2019, 7, 54–59.

Xi, C.; Chen, J.; Zhao, C.; Pei, Q.; Liu, L., “Real-time Hand Tracking Using Kinect.”, In Proceedings of the 2nd International Conference on Digital Signal Processing, Tokyo, Japan, pp. 37–42,25–27 February 2018.

Devineau, G.; Moutarde, F.; Xi, W.; Yang, J., “Deep learning for hand gesture recognition on skeletal data.”, In Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi’an, China, pp. ,106–113, 15–19 May 2018.

Konstantinidis, D.; Dimitropoulos, K.; Daras, P. , “Sign language recognition based on hand and body skeletal data.”, In Proceedings of the 2018-3DTV-Conference: The True Vision-Capture, Transmission and Displasy of 3D Video (3DTV-CON), Helsinki, Finland, pp. 1–4, 3–5 June 2018.

De Smedt, Q.; Wannous, H.; Vandeborre, J.-P.; Guerry, J.; Saux, B.L.; Filliat, D., “3D hand gesture recognition using a depth and skeletal dataset: SHREC’17 track.” ,In Proceedings of the Workshop on 3D Object Retrieval, Lyon, France, pp. 33–38, 23–24 April 2017.

Karbasi, M.; Muhammad, Z.; Waqas, A.; Bhatti, Z.; Shah, A.; Koondhar, M.Y.; Brohi, I.A. “A Hybrid Method Using Kinect Depth and Color Data Stream for Hand Blobs Segmentation”; Science International: Lahore, Pakistan, Volume 29, pp. 515–519, 2017.

Ma, X.; Peng, “J. Kinect sensor-based long-distance hand gesture recognition and fingertip detection with depth information.”, J. Sens. 5809769, 2018.

Desai, “S. Segmentation and Recognition of Fingers Using Microsoft Kinect”, In Proceedings of the International Conference on Communication and Networks, Paris, France, pp. 45–53, 21–25 May 2017.

Wan, C.; Probst, T.; Van Gool, L.; Yao, A., “Self-supervised 3d hand pose estimation through training by fitting.”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 10853–10862, 15–20 June 2019.

Tekin, B.; Bogo, F.; Pollefeys, M., “H+ O: Unified egocentric recognition of 3D hand-object poses and interactions.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 4511–4520, 15–20 June 2019.

Ge, L.; Ren, Z.; Li, Y.; Xue, Z.; Wang, Y.; Cai, J.; Yuan, J. “3d hand shape and pose estimation from a single rgb image”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 10833–10842, 15–20 June 2019.