Abstract
Vision-based hand gesture recognition is perhaps the most widely used technique for human–computer interaction technology with special application toward sign languages used by differently abled people. Perhaps the most important step in such a gesture recognition system is feature extraction. In this paper, we use a novel fingertip detection mechanism based on convexity defects and use nine geometrical features that are translation and rotation invariant. We use seven different classifiers on two different public hand digit datasets (NTU hand digit dataset and SP-EMD color-depth hand gesture dataset) and find that for both the datasets, the random forest classifier gives the best classification accuracy (94.2% and 90.1%, respectively).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mitra S, Acharya T (2007) Gesture recognition: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 37(3):311–324
Shi B, Brentari D, Shakhnarovich G, Livescu K (2021) Fingerspelling detection in American sign language. ar**v preprint ar**v:2104.01291
Hussain SMA, Rashid AH (2012) User independent hand gesture recognition by accelerated DTW. In: 2012 international conference on informatics, electronics and vision (ICIEV). IEEE, 1033–1037
Choi J, Seo BK, Lee D, Park H, Park JI (2013) Rgb-d camera-based hand shape recognition for human-robot interaction. In: IEEE ISR 2013. IEEE, 1–2
Palacios JM, Sagüés C, Montijano E, Llorente S (2013) Human-computer interaction based on hand gestures using rgb-d sensors. Sensors 13(9):11842–11860
Tiantian L, **yuan S, Runjie L, Yingying G (2015) Hand gesture recognition based on improved histograms of oriented gradients. In: The 27th Chinese control and decision conference (2015 CCDC), 4211–4215
Jiang F, Wang C, Gao Y, Wu S, Zhao D (2015) Discriminating features learning in hand gesture classification. IET Comput Vis 9(5):673–680
Panwar M (2012) Hand gesture recognition based on shape parameters. In: 2012 international conference on computing, communication and applications, 1–6
Mesbahi SC, Mahraz MA, Riffi J, Tairi H (2018) Hand gesture recognition based on convexity approach and background subtraction. In: 2018 international conference on intelligent systems and computer vision (ISCV). IEEE, 1–5
Ganapathyraju S (2013) Hand gesture recognition using convexity hull defects to control an industrial robot. In: 2013 3rd international conference on instrumentation control and automation (ICA). IEEE, 63–67
Maisto M, Panella M, Liparulo L, Proietti A (2013) An accurate algorithm for the identification of fingertips using an rgb-d camera. IEEE J Emerg Sel Top Circ Syst 3(2):272–283
Plouffe G, Cretu AM (2015) Static and dynamic hand gesture recognition in depth data using dynamic time war**. IEEE Trans Instrum Measur 65(2):305–316
Ma X, Peng J (2018) Kinect sensor-based long-distance hand gesture recognition and fingertip detection with depth information. J Sens
Xu Y, Park DW, Pok G (2017) Hand gesture recognition based on convex defect detection. Int J Appl Eng Res 12(18):7075–7079
Xu Y, Dai Y (2018) Convex defect detection and density distribution based hand gesture recognition. J Comput 29(5):29–40
Kakkoth SS, Gharge, S (2018) Visual descriptors based real time hand gesture recognition. In: International conference on advances in communication and computing technology (ICACCT). IEEE, 361–367
Hartanto R, Kartikasari A (2016) Android based real-time static Indonesian sign language recognition system prototype. In: 2016 8th international conference on information technology and electrical engineering (ICITEE). IEEE, 1–6
Li Y (2012) Multi-scenario gesture recognition using kinect. In: 2012 17th international conference on computer games (CGAMES). IEEE, 126–130
Saha HN, Tapadar S, Ray S, Chatterjee SK, Saha S (2018) A machine learning based approach for hand gesture recognition using distinctive feature extraction. In: IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE, 91–98
Ren Z, Meng J, Yuan J (2011) Depth camera based hand gesture recognition and its applications in human-computer-interaction. In: 2011 8th international conference on information, communications and signal processing (ICICS). IEEE, 1–5
Ren Z, Yuan J, Meng J, Zhang Z (2013) Robust part-based hand gesture recognition using kinect sensor. IEEE Trans Multimedia 15(5):1110–1120
Wang C, Chan S (2014) A new hand gesture recognition algorithm based on joint color-depth superpixel earth mover’s distance. In: 4th international workshop on cognitive information processing (CIP). IEEE, 1–6
Wang C, Liu Z, Chan SC (2014) Superpixel-based hand gesture recognition with kinect depth camera. IEEE Trans Multimedia 17(1):29–39
Paul S, Basu S, Nasipuri M (2015) Microsoft kinect in gesture recognition: a short review. Int J Control Theory Appl 8(5):20712076
Paul S, Bhattacharyya A, Mollah AF, Basu S, Nasipuri M (2019) Hand segmentation from complex background for gesture recognition. In: Emerging technology in modelling and graphics. Springer Singapore, 775–782
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Paul, S., Gangopadhyay, S., Mollah, A.F., Basu, S., Nasipuri, M. (2023). Convexity Defects-Based Fingertip Detection and Hand Gesture Recognition. In: Basu, S., Kole, D.K., Maji, A.K., Plewczynski, D., Bhattacharjee, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Lecture Notes in Networks and Systems, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-19-0105-8_21
Download citation
DOI: https://doi.org/10.1007/978-981-19-0105-8_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0104-1
Online ISBN: 978-981-19-0105-8
eBook Packages: EngineeringEngineering (R0)