Open Access Open Access  Restricted Access Subscription or Fee Access

Vision Based Gesture Recognition Techniques: A Review

Avinash S Pathare, Supriya O Rajankar


With the ever-growing use of computers in the society, it is clearly seen that present popular mode of interactions with computers is mouse and keyboard. Mouse and keyboard will become an inconvenient way to convey information to the computers. Nowadays there are intelligent systems in which the flow of information takes place without any physical contact. The vision based (camera) gesture recognition has the potential to be a natural and powerful tool supporting efficient and spontaneous interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human-Computer Interaction (HCI). HCI has driven many researchers in computer vision-based analysis and understanding of hand gestures as a very active research area. This paper reviews some major developments and the recent evolution in the field of hand gesture recognition techniques. While providing a Non-Exhaustive catalog of the huge amount of past research in the field, the paper reviews in more detail vision-based approaches. We will discuss several traditional and new applications of gesture recognition in this paper. We surveyed the literature on the visual analysis of hand gestures in the context of its role in HCI and emphasized numerous important works of researchers. The tenacity of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers. The aim of hand gesture recognition researchers is to develop such a system that will easily identify gestures and use them for a variety of application ranging from sign language to virtual reality.

Cite this Article
Avinash S. Pathare, Supriya O. Rajankar. Vision Based Gesture Recognition Techniques: A Review. Journal of Artificial Intelligence Research & Advances. 2015; 2(3): 1–8p.


Hand gestures, human-computer interaction, model-based approach, recognition, vision-based approach

Full Text:



Konstantinos G. Derpanis. A review of vision-based hand gestures. 2004; 1–18p

New JR, Hasanbelliu E, Aguilar M. Facilitating user interaction with complex systems via hand gesture recognition. In Proc of ACM Conf. 2003.

Mo Z, Lewis JP, Neumann U. Smartcanvas: a gesture-driven intelligent drawing desk system. International Conference on Intelligent User Interfaces. 2005; 239–243p.

Martin J, Devin V, Crowley JL. Active hand tracking. 3rd. International Conference on Face & Gesture Recognition in IEEE. 1998; 575p.

Kjeldsen R, Kender J. Toward the use of gesture in traditional user interfaces. International Conference on Automatic Face and Gesture Recognition in IEEE. 1996; 151–156p.

Bolt RA. Voice and gesture at the graphics interface. Proc. SIGGRAPH80. 1980; 14(3): 262–270p.

Wang CC, Wang KC. Hand posture recognition using adaboost with SIFT for human-robot interaction. Springer. 2008; 370: 317–329p.

Lowe DG. Object recognition from local scale-invariant features (SIFT). Proc. of the International Conference on Computer Vision. 1999; 2: 1150–1157p.

Barczak ALC, Dadgostar F. Real-time hand tracking using a set of co-operative classifiers based on haar-like features. Res. Lett Inf. Math and Sci. 2005; 7: 29–42p.

Chen Q, Georganas ND, Petriu EM. Real-time vision-based hand gesture recognition using haar-like features. IEEE Trans. on Instrumentation and Measurement. 2007; 1–6p.

Donoser M, Bischof H. Real-time appearance-based hand tracking pattern. In a Conference of ICPR. 2008; 1–4p.

Mikolajczyk K, Schmid C, Zisserman A et al. A comparison of affine region detectors. In International Journal of Computer Vision. 2005; 65(1/2): 43–72p.

Bay M, Koller-Meier, Gool LV. Smart particle filtering for 3D hand tracking. 6th IEEE Conference on Automatic Face and Gesture Recognition. 2004; 675p.

Heap AJ, Hogg DC. Towards 3-D hand tracking using a deformable model. In 2nd International Conference on Face and Gesture Recognition. 1996; 140–145p.

Stenger B, Mendon PRS, Cipolla R. Model-based 3D tracking of an articulated hand. Proc. In British Machine Vision Conference. 2001; 2: 63–72p.

Stenger B, Thayananthan A, Torr PHS et al. Model-based hand tracking using a hierarchical bayesian filter. In IEEE Trans. on Pattern Analysis and Machine Intelligence. 2006; 28(9): 1372–1384p.

Rehg J, Kanade T. Visual tracking of high DoF articulated structures: An application to human hand tracking. European Conference on Computer Vision and Image Understanding. 1994; 801: 35–46p.

La Gorce M de, Paragios N, Fleet DJ. Model-based hand tracking with texture, shading, and self-occlusions. In IEEE Conference on Computer Vision and Pattern Recognition. 2008; 1–8p.

La Gorce M de, Fleet DJ, Paragios N. Model-based 3D hand pose estimation from monocular video. In IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011; 33(9): 1793–1805p.

Dong Guo Yonghua. Vision-based hand gesture recognition for human-vehicle interaction. International Conference on Control, Automation and Computer Vision. 1998.

Smith GM, Schraefel MC. Scrolling support for stylus/touch-based document navigation. In Proc. seventeenth ACM Symposium on User Interface Software and Technology. 2004; 53–56p.

Jinshi Cui, Zengqi Sun. Model-based visual hand posture tracking for guiding a dexterous robotic hand. Optics Communications. 2004; 235: 311–318p.

Wachs PJ, Stern HI, Edan Y et al. A gesture-based tool for sterile browsing of radiology images. A Journal of the American Medical Informatics Association. 2008; 15(3): 321–323p.


  • There are currently no refbacks.

This site has been shifted to