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A systematic literature review on vision based gesture recognition techniques

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Abstract

Human Computer Interaction (HCI) technologies are rapidly evolving the way we interact with computing devices and adapting to the constantly increasing demands of modern paradigms. One of the most useful tools in this regard is the integration of Human-to-Human Interaction gestures to facilitate communication and expressing ideas. Gesture recognition requires the integration of postures, gestures, face expressions and movements for communicating or conveying certain messages. The aim of this study is to aggregate and synthesize experiences and accumulated knowledge about Vision-Based Recognition (VBR) techniques. The major objective of conducting this Systematic Literature Review (SLR) is to highlight the state-of-the-art in the context of vision-based gesture recognition with specific focus on hand gesture recognition (HGR) techniques and enabling technologies. After a careful systematic selection process, 100 studies relevant to the four research questions were selected. This process was followed by data collection, a detailed analysis, and a synthesis of the selected studies. The results reveal that among the VBR techniques, HGR is a predominant and highly focused area of research. Research focus is also found to be converging towards sign language recognition. Potential applications of HGR techniques include desktop applications, smart environments, entertainment, sign language interpretation, virtual reality and gamification. Although various experimental research efforts have been devoted to gestures recognition, there are still numerous open issues and research challenges in this field. Lastly, considering the results from this SLR, potential future research directions are suggested, including a much needed focus on grammatical interpretation, hybrid approaches, smartphone devices, normalization, and real-life systems.

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Acknowledgments

We would like to extend our appreciation to University of Malaya and The University of Jordan for providing all necessary support to conduct this research work.

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Authors and Affiliations

  1. Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Ahmad Sami Al-Shamayleh, Rodina Ahmad, Khubaib Amjad Alam & Nazean Jomhari

  2. Department of Computer Information Systems, King Abdullah II School of Information Technology, The University of Jordan, Amman, 11942, Jordan

    Mohammad A. M. Abushariah

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Correspondence to Ahmad Sami Al-Shamayleh.

Appendix

Appendix

This SLR was aimed to aggregate and synthesize accumulated knowledge and experience in the context of gesture recognition and vision-based hand gesture recognition. Topics discussed included hand gesture analysis approaches, taxonomies of hand gesture recognition approaches, techniques and algorithms, a taxonomy of open issues and challenges, as well as potential technological improvements, and future research trends and directions. Publications in the hand gesture recognition field were investigated and scanned widely, including accredited scientific journals, conference proceedings, workshops and online reports. The investigation was carried out in English. All publications evaluated were split into four categories, as shown in Fig. 21. For this research, the proportion of scientific journals perused was 60% of all references employed. In addition, the proportion of conference proceedings was 36%. Finally, workshop papers and theses comprised 2% of all references used. Figure 22 presents a chronological distribution of studies over the years. Figure 23 illustrates a statistical analysis of hand gesture recognition solutions according to contribution type.

Fig. 21
figure 21

References by category specifically dedicated to gesture recognition from over 100 scientific documents evaluated

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Fig. 22
figure 22

Chronological distribution of studies over the years

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Fig. 23
figure 23

Statistical analysis of hand gesture recognition solutions according to contribution type

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Table 21 Studies used to answer the research questions on hand gestures recognition from 1st January 1996 to 31th October 2016
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Table 22 Studies distribution per publication sources
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Al-Shamayleh, A.S., Ahmad, R., Abushariah, M.A.M. et al. A systematic literature review on vision based gesture recognition techniques. Multimed Tools Appl 77, 28121–28184 (2018). https://doi.org/10.1007/s11042-018-5971-z

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