Skip to main content

Advertisement

Log in

Survey on vision-based dynamic hand gesture recognition

  • Survey
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

To communicate with one another hand, gesture is very important. The task of using the hand gesture in technology is influenced by a very common way humans communicate with the natural environment. The recognizing and finding pose estimation of hand comes under the area of hand gesture analysis. To find out the gesturing hand is very difficult than finding the another part of the human body because the hand is smaller in size. The hand has greater complexity and more challenges due to differences between the cultural or individual factors of users and gestures invented from ad hoc. The complication and divergences of finding hand gestures will deeply affect the recognition rate and accuracy. This paper emphasizes on summary of hand gestures technique, recognition methods, merits and demerits, various applications, available data sets, and achieved accuracy rate, classifiers, algorithm, and gesture types. This paper also scrutinizes the performance of traditional and deep learning methods on dynamic hand gesture recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

All the dataset link is provided in the paper.

Notes

  1. http://www-rech.telecom-lille.fr/DHGdataset/.

  2. http://lshao.staff.shef.ac.uk/data/SheffieldKinectGesture.htm.

  3. https://research.nvidia.com/publication/online-detection-and-classification-dynamic-hand-gestures-recurrent-3d-convolutional.

  4. https://guiggh.github.io/publications/first-person-hands/.

  5. http://cvrr.ucsd.edu/LISA/hand.htm.

  6. ftp://mi.eng.cam.ac.uk/pub/CamGesData.

  7. https://github.com/davidespano/3cent-dataset.

  8. https://github.com/Ha0Tang/HandGestureRecognition/blob/master/README.md.

  9. http://www.nlpr.ia.ac.cn/iva/yfzhang/datasets/egogesture.html.

References

  1. Pranjali, S., Ubale, V.: Hand gesture recognition system: a survey. International journal of Inventive Engineering and Science (IJIES), ISSN, 2319–9598 (2015)

  2. Rautaray, S.S.: Real time hand gesture recognition system for dynamic applications. Int. J. UbiComp (IJU) 3(1), 11 (2012)

    Google Scholar 

  3. Oudah, M., Al-Naji, A., Chahl, J.: Hand gesture recognition based on computer vision: a review of techniques. J. Imag. 6(8), 73 (2020)

    Article  Google Scholar 

  4. Tsai, T.-H., Luo, Y.-J., Wan, W.-C.: A skeleton-based dynamic hand gesture recognition for home appliance control system. In: 2022 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 3265–3268 (2022). IEEE

  5. Mohammed, A.A., Lv, J., Islam, M.S., Sang, Y.: Multi-model ensemble gesture recognition network for high-accuracy dynamic hand gesture recognition. J. Ambient Intell. Human. Comput. 14(6), 6829–6842 (2022)

    Article  Google Scholar 

  6. Verma, B., Choudhary, A.: Framework for dynamic hand gesture recognition using grassmann manifold for intelligent vehicles. IET Intell. Transp. Syst. 12(7), 721–729 (2018)

    Article  Google Scholar 

  7. Ohn-Bar, E., Trivedi, M.M.: Hand gesture recognition in real time for automotive interfaces: a multimodal vision-based approach and evaluations. IEEE Trans. Intell. Transp. Syst. 15(6), 2368–2377 (2014)

    Article  Google Scholar 

  8. Sykora, P., Kamencay, P., Hudec, R.: Comparison of sift and surf methods for use on hand gesture recognition based on depth map. Aasri Procedia 9, 19–24 (2014)

    Article  Google Scholar 

  9. Verma, B., Choudhary, A.: Grassmann manifold based dynamic hand gesture recognition using depth data. Multimed. Tools Appl. 79(3), 2213–2237 (2020)

    Article  Google Scholar 

  10. Wang, H., Kläser, A., Schmid, C., Liu, C.-L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)

    Article  MathSciNet  Google Scholar 

  11. Suarez, J., Murphy, R.R.: Hand gesture recognition with depth images: A review. In: 2012 IEEE RO-MAN: the 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 411–417 (2012). IEEE

  12. Pisharady, P.K., Saerbeck, M.: Recent methods and databases in vision-based hand gesture recognition: a review. Comput. Vis. Image Understand. 141, 152–165 (2015)

    Article  Google Scholar 

  13. Yasen, M., Jusoh, S.: A systematic review on hand gesture recognition techniques, challenges and applications. PeerJ Computer Science 5, 218 (2019)

    Article  Google Scholar 

  14. Cheok, M.J., Omar, Z., Jaward, M.H.: A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. Cybernet. 10, 131–153 (2019)

    Article  Google Scholar 

  15. Pavlovic, V.I., Sharma, R., Huang, T.S.: Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 677–695 (1997)

    Article  Google Scholar 

  16. Guo, L., Lu, Z., Yao, L.: Human-machine interaction sensing technology based on hand gesture recognition: a review. IEEE Trans. Human-Mach. Syst. 51(4), 300–309 (2021)

    Article  Google Scholar 

  17. Sarma, D., Kavyasree, V., Bhuyan, M.K.: Two-stream fusion model for dynamic hand gesture recognition using 3d-cnn and 2d-cnn optical flow guided motion template. arXiv preprint arXiv:2007.08847 (2020)

  18. Verma, B., Choudhary, A.: Affective state recognition from hand gestures and facial expressions using Grassmann manifolds. Multimed. Tools Appl. 80(9), 14019–14040 (2021)

    Article  Google Scholar 

  19. Bilen, H., Fernando, B., Gavves, E., Vedaldi, A.: Action recognition with dynamic image networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2799–2813 (2017)

    Article  Google Scholar 

  20. Nguyen, X.S., Brun, L., Lézoray, O., Bougleux, S.: A neural network based on spd manifold learning for skeleton-based hand gesture recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12036–12045 (2019)

  21. Zhou, L., Bai, X., Liu, X., Zhou, J., Hancock, E.R.: Learning binary code for fast nearest subspace search. Pattern Recognit. 98, 107040 (2020)

    Article  Google Scholar 

  22. De Smedt, Q., Wannous, H., Vandeborre, J.-P.: Skeleton-based dynamic hand gesture recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–9 (2016)

  23. Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)

    Article  Google Scholar 

  24. Conseil, S., Bourennane, S., Martin, L.: Comparison of fourier descriptors and hu moments for hand posture recognition. In: 2007 15th European Signal Processing Conference, pp. 1960–1964 (2007). IEEE

  25. Kollorz, E., Penne, J., Hornegger, J., Barke, A.: Gesture recognition with a time-of-flight camera. Int. J. Intell. Syst. Technol. Appl. 5(3–4), 334–343 (2008)

    Google Scholar 

  26. Kane, L., Khanna, P.: Depth matrix and adaptive bayes classifier based dynamic hand gesture recognition. Pattern Recognit. Lett. 120, 24–30 (2019)

    Article  Google Scholar 

  27. Tang, H., Liu, H., Xiao, W., Sebe, N.: Fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion. Neurocomputing 331, 424–433 (2019)

    Article  Google Scholar 

  28. Zhang, C., Wang, Z., An, Q., Li, S., Hoorfar, A., Kou, C.: Clustering-driven dgs-based micro-doppler feature extraction for automatic dynamic hand gesture recognition. Sensors 22(21), 8535 (2022)

    Article  Google Scholar 

  29. Benitez-Garcia, G., Prudente-Tixteco, L., Castro-Madrid, L.C., Toscano-Medina, R., Olivares-Mercado, J., Sanchez-Perez, G., Villalba, L.J.G.: Improving real-time hand gesture recognition with semantic segmentation. Sensors 21(2), 356 (2021)

    Article  Google Scholar 

  30. Liang, H., Yuan, J., Thalmann, D., Zhang, Z.: Model-based hand pose estimation via spatial-temporal hand parsing and 3d fingertip localization. Vis. Comput. 29, 837–848 (2013)

    Article  Google Scholar 

  31. Wu, H., Wang, J., Zhang, X.: Combining hidden markov model and fuzzy neural network for continuous recognition of complex dynamic gestures. Vis. Comput. 33, 1265–1278 (2017)

    Article  Google Scholar 

  32. Wu, H., Wang, J.: A visual attention-based method to address the midas touch problem existing in gesture-based interaction. Vis. Comput. 32, 123–136 (2016)

    Article  Google Scholar 

  33. Li, J., Liu, R., Kong, D., Wang, S., Wang, L., Yin, B., Gao, R.: Attentive 3d-ghost module for dynamic hand gesture recognition with positive knowledge transfer. Comput. Intell. Neurosci. 2021, 1–12 (2021)

    Google Scholar 

  34. Chen, G., Dong, Z., Wang, J., Xia, L.: Parallel temporal feature selection based on improved attention mechanism for dynamic gesture recognition. Complex Intell. Syst. 9(2), 1377–1390 (2023)

    Article  Google Scholar 

  35. Li, C., Li, S., Gao, Y., Zhang, X., Li, W.: A two-stream neural network for pose-based hand gesture recognition. arXiv preprint arXiv:2101.08926 (2021)

  36. Li, J., Wei, L., Wen, Y., Liu, X., Wang, H.: An approach to continuous hand movement recognition using semg based on features fusion. Vis. Comput. 39(5), 2065–2079 (2023)

    Article  Google Scholar 

  37. Li, D., Chen, Y., Gao, M., Jiang, S., Huang, C.: Multimodal gesture recognition using densely connected convolution and blstm. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3365–3370 (2018). IEEE

  38. Ma, C., Wang, A., Chen, G., Xu, C.: Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network. Vis. Comput. 34(6), 1053–1063 (2018)

    Article  Google Scholar 

  39. Ameur, S., Khalifa, A.B., Bouhlel, M.S.: A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with leap motion. Entertain. Comput. 35, 100373 (2020)

    Article  Google Scholar 

  40. Hou, J., Wang, G., Chen, X., Xue, J.-H., Zhu, R., Yang, H.: Spatial-temporal attention res-tcn for skeleton-based dynamic hand gesture recognition. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pp. 0–0 (2018)

  41. Zhang, X., Li, X.: Dynamic gesture recognition based on MEMP network. Fut. Internet 11(4), 91 (2019)

    Article  Google Scholar 

  42. Zeghoud, S., Ali, S.G., Ertugrul, E., Kamel, A., Sheng, B., Li, P., Chi, X., Kim, J., Mao, L.: Real-time spatial normalization for dynamic gesture classification. Vis. Comput. 38, 1345–1357 (2022)

    Article  Google Scholar 

  43. Tsinganos, P., Cornelis, B., Cornelis, J., Jansen, B., Skodras, A.: Hilbert SEMG data scanning for hand gesture recognition based on deep learning. Neural Comput. Appl. 33(7), 2645–2666 (2021)

    Article  Google Scholar 

  44. Lin, H.-I., Hsu, M.-H., Chen, W.-K.: Human hand gesture recognition using a convolution neural network. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1038–1043 (2014). IEEE

  45. Li, J., Huai, H., Gao, J., Kong, D., Wang, L.: Spatial-temporal dynamic hand gesture recognition via hybrid deep learning model. J. Multimodal User Interfaces 13(4), 363–371 (2019)

    Article  Google Scholar 

  46. Mujahid, A., Awan, M.J., Yasin, A., Mohammed, M.A., Damaševičius, R., Maskeliūnas, R., Abdulkareem, K.H.: Real-time hand gesture recognition based on deep learning yolov3 model. Appl. Sci. 11(9), 4164 (2021)

    Article  Google Scholar 

  47. Mohammed, A.A.Q., Lv, J., Islam, M.: A deep learning-based end-to-end composite system for hand detection and gesture recognition. Sensors 19(23), 5282 (2019)

    Article  Google Scholar 

  48. Yang, L., Chen, J., Zhu, W.: Dynamic hand gesture recognition based on a leap motion controller and two-layer bidirectional recurrent neural network. Sensors 20(7), 2106 (2020)

    Article  Google Scholar 

  49. 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)

  50. Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J.: Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4207–4215 (2016)

  51. Köpüklü, O., Gunduz, A., Kose, N., Rigoll, G.: Real-time hand gesture detection and classification using convolutional neural networks. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1–8 (2019). IEEE

  52. Köpüklü, O., Gunduz, A., Kose, N., Rigoll, G.: Online dynamic hand gesture recognition including efficiency analysis. IEEE Trans. Biomet. Behav. Identity Sci. 2(2), 85–97 (2020)

    Article  Google Scholar 

  53. Zhang, Z., Tian, Z., Zhou, M.: Latern: dynamic continuous hand gesture recognition using FMCW radar sensor. IEEE Sensors J. 18(8), 3278–3289 (2018)

    Article  Google Scholar 

  54. Benitez-Garcia, G., Olivares-Mercado, J., Sanchez-Perez, G., Yanai, K.: Ipn hand: A video dataset and benchmark for real-time continuous hand gesture recognition. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4340–4347 (2021). IEEE

  55. Gao, Q., Chen, Y., Ju, Z., Liang, Y.: Dynamic hand gesture recognition based on 3d hand pose estimation for human-robot interaction. IEEE Sensors J. 22(18), 17421–17430 (2021)

    Article  Google Scholar 

  56. Verma, P., Sah, A., Srivastava, R.: Deep learning-based multi-modal approach using RGB and skeleton sequences for human activity recognition. Multimed. Syst. 26(6), 671–685 (2020)

    Article  Google Scholar 

  57. Chen, X., Guo, H., Wang, G., Zhang, L.: Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2881–2885 (2017). IEEE

  58. Lai, K., Yanushkevich, S.N.: Cnn+ rnn depth and skeleton based dynamic hand gesture recognition. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3451–3456 (2018). IEEE

  59. Shi, L., Zhang, Y., Cheng, J., Lu, H.: Decoupled spatial-temporal attention network for skeleton-based action-gesture recognition. In: Proceedings of the Asian Conference on Computer Vision (2020)

  60. Liu, J., Liu, Y., Wang, Y., Prinet, V., Xiang, S., Pan, C.: Decoupled representation learning for skeleton-based gesture recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5751–5760 (2020)

  61. Devineau, G., Moutarde, F., Xi, W., Yang, J.: Deep learning for hand gesture recognition on skeletal data. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 106–113 (2018). IEEE

  62. Li, Y., Ma, D., Yu, Y., Wei, G., Zhou, Y.: Compact joints encoding for skeleton-based dynamic hand gesture recognition. Comput. Graph. 97, 191–199 (2021)

    Article  Google Scholar 

  63. Li, Y., He, Z., Ye, X., He, Z., Han, K.: Spatial temporal graph convolutional networks for skeleton-based dynamic hand gesture recognition. EURASIP J. Image Video Process. 2019(1), 1–7 (2019)

    Article  Google Scholar 

  64. Chen, X., Wang, G., Guo, H., Zhang, C., Wang, H., Zhang, L.: MFA-net: motion feature augmented network for dynamic hand gesture recognition from skeletal data. Sensors 19(2), 239 (2019)

    Article  Google Scholar 

  65. Mahmud, H., Morshed, M.M., Hasan, M.K.: Quantized depth image and skeleton-based multimodal dynamic hand gesture recognition. The Visual Computer, 1–15 (2023)

  66. Peng, S.-H., Tsai, P.-H.: An efficient graph convolution network for skeleton-based dynamic hand gesture recognition. In: IEEE Transactions on Cognitive and Developmental Systems (2023)

  67. Li, Y., Ma, D., Yu, Y., Wei, G., Zhou, Y.: Compact joints encoding for skeleton-based dynamic hand gesture recognition. Comput. Graph. 97, 191–199 (2021)

    Article  Google Scholar 

  68. Zhang, Y., Cao, C., Cheng, J., Lu, H.: Egogesture: a new dataset and benchmark for egocentric hand gesture recognition. IEEE Trans. Multimed. 20(5), 1038–1050 (2018)

    Article  Google Scholar 

  69. Dhingra, N., Kunz, A.: Res3atn-deep 3d residual attention network for hand gesture recognition in videos. In: 2019 International Conference on 3D Vision (3DV), pp. 491–501 (2019). IEEE

  70. Cao, Z., Li, Y., Shin, B.-S.: Content-adaptive and attention-based network for hand gesture recognition. Appl. Sci. 12(4), 2041 (2022)

    Article  Google Scholar 

  71. Yu, Z., Zhou, B., Wan, J., Wang, P., Chen, H., Liu, X., Li, S.Z., Zhao, G.: Searching multi-rate and multi-modal temporal enhanced networks for gesture recognition. IEEE Trans. Image Process. 30, 5626–5640 (2021)

    Article  Google Scholar 

  72. Abavisani, M., Joze, H.R.V., Patel, V.M.: Improving the performance of unimodal dynamic hand-gesture recognition with multimodal training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1165–1174 (2019)

  73. Hu, B., Wang, J.: Deep learning based hand gesture recognition and UAV flight controls. Int. J. Automat. Comput. 17(1), 17–29 (2020)

    Article  Google Scholar 

  74. Mishra, S.: Infant hand detection and tracking (2021)

  75. Breland, D.S., Skriubakken, S.B., Dayal, A., Jha, A., Yalavarthy, P.K., Cenkeramaddi, L.R.: Deep learning-based sign language digits recognition from thermal images with edge computing system. IEEE Sensors J. 21(9), 10445–10453 (2021)

    Article  Google Scholar 

  76. D’Eusanio, A., Simoni, A., Pini, S., Borghi, G., Vezzani, R., Cucchiara, R.: Multimodal hand gesture classification for the human–car interaction. In: Informatics, vol. 7, p. 31 (2020). Multidisciplinary Digital Publishing Institute

  77. Hakim, N.L., Shih, T.K., Kasthuri Arachchi, S.P., Aditya, W., Chen, Y.-C., Lin, C.-Y.: Dynamic hand gesture recognition using 3dCNN and LSTM with FSM context-aware model. Sensors 19(24), 5429 (2019)

    Article  Google Scholar 

  78. Nasri, N., Orts-Escolano, S., Cazorla, M.: An SEMG-controlled 3d game for rehabilitation therapies: Real-time time hand gesture recognition using deep learning techniques. Sensors 20(22), 6451 (2020)

    Article  Google Scholar 

  79. Abdallah, M.S., Samaan, G.H., Wadie, A.R., Makhmudov, F., Cho, Y.-I.: Light-weight deep learning techniques with advanced processing for real-time hand gesture recognition. Sensors 23(1), 2 (2022)

    Article  Google Scholar 

  80. Jain, R., Karsh, R.K., Barbhuiya, A.A.: Encoded motion image-based dynamic hand gesture recognition. Vis. Comput. 38(6), 1957–1974 (2022)

    Article  Google Scholar 

  81. Mahmud, H., Islam, R., Hasan, M.K.: On-air english capital alphabet (eca) recognition using depth information. Vis. Comput. 38(3), 1015–1025 (2022)

    Article  Google Scholar 

  82. Zhang, W., Lin, Z., Cheng, J., Ma, C., Deng, X., Wang, H.: Sta-GCN: two-stream graph convolutional network with spatial-temporal attention for hand gesture recognition. Vis. Comput. 36, 2433–2444 (2020)

    Article  Google Scholar 

  83. Wang, R.Y., Popović, J.: Real-time hand-tracking with a color glove. ACM Trans. Graph. (TOG) 28(3), 1–8 (2009)

    Google Scholar 

  84. Aljawaryy, A., Malallah, L.: Real-time numerical 0–5 counting based on hand-finger gestures recognition. J. Theor. Appl. Inf. Technol. 95(13), 3105 (2017)

    Google Scholar 

  85. Mahanama, B., Jayawardana, Y., Jayarathna, S.: Gaze-net: Appearance-based gaze estimation using capsule networks. In: Proceedings of the 11th Augmented Human International Conference, pp. 1–4 (2020)

  86. Grzejszczak, T., Niezabitowski, M.: Applications of hand feature points detection and localization algorithms. In: MATEC Web of Conferences, vol. 56, p. 02009 (2016). EDP Sciences

  87. Aghajari, E., Gharpure, D.: Real time vision-based hand gesture recognition for robotic application. Int.J. Adv. Res. Comput. Sci. Softw. Eng 4(3), 2277–128 (2014)

    Google Scholar 

  88. Verma, B.: A two stream convolutional neural network with bi-directional GRU model to classify dynamic hand gesture. J. Vis. Commun. Image Represent. 87, 103554 (2022)

    Article  Google Scholar 

  89. Bamwenda, J., Özerdem, M.S.: Recognition of static hand gesture with using ann and svm. Dicle University Journal of Engineering (2019)

  90. Paul, S., Nasser, H., Mollah, A.F., Bhattacharyya, A., Ngo, P., Nasipuri, M., Debled-Rennesson, I., Basu, S.: Development of benchmark datasets of multioriented hand gestures for speech and hearing disabled. Multimed. Tools Appl. 81(5), 7285–7321 (2022)

    Article  Google Scholar 

  91. Song, L., Hu, R.M., Zhang, H., Xiao, Y.L., Gong, L.Y.: Real-time 3d hand gesture detection from depth images. Adv. Mater. Res. 756, 4138–4142 (2013)

    Article  Google Scholar 

  92. Tang, M.: Recognizing hand gestures with microsoft’s kinect. Palo Alto: Department of Electrical Engineering of Stanford University:[sn] (2011)

  93. Tekin, B., Bogo, F., Pollefeys, M.: H+ o: Unified egocentric recognition of 3d hand-object poses and interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4511–4520 (2019)

  94. Wan, C., Probst, T., Gool, L.V., Yao, A.: Self-supervised 3d hand pose estimation through training by fitting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10853–10862 (2019)

  95. 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/CVF Conference on Computer Vision and Pattern Recognition, pp. 10833–10842 (2019)

  96. Alnaim, N., Abbod, M., Albar, A.: Hand gesture recognition using convolutional neural network for people who have experienced a stroke. In: 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–6 (2019). IEEE

  97. Chung, H.-Y., Chung, Y.-L., Tsai, W.-F.: An efficient hand gesture recognition system based on deep cnn. In: 2019 IEEE International Conference on Industrial Technology (ICIT), pp. 853–858 (2019). IEEE

  98. Wu, X.Y.: A hand gesture recognition algorithm based on dc-CNN. Multimedia Tools Appl. 79(13), 9193–9205 (2020)

    Article  Google Scholar 

  99. Stergiopoulou, E., Sgouropoulos, K., Nikolaou, N., Papamarkos, N., Mitianoudis, N.: Real time hand detection in a complex background. Eng. Appl. Artif. Intell. 35, 54–70 (2014)

    Article  Google Scholar 

  100. Khandade, S.L., Khot, S.: Matlab based gesture recognition. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol. 1, pp. 1–4 (2016). IEEE

  101. Karabasi, M., Bhatti, Z., Shah, A.: A model for real-time recognition and textual representation of malaysian sign language through image processing. In: 2013 International Conference on Advanced Computer Science Applications and Technologies, pp. 195–200 (2013). IEEE

  102. Fang, Y., Wang, K., Cheng, J., Lu, H.: A real-time hand gesture recognition method. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 995–998 (2007). IEEE

  103. Licsár, A., Szirányi, T.: User-adaptive hand gesture recognition system with interactive training. Image Vis. Comput. 23(12), 1102–1114 (2005)

    Article  Google Scholar 

  104. Pun, C.-M., Zhu, H.-M., Feng, W.: Real-time hand gesture recognition using motion tracking. Int. J. Comput. Intell. Syst. 4(2), 277–286 (2011)

    Google Scholar 

  105. Phuong, H.N., Thi, M.T.D.: An approach in building a vision-based hand gesture recognition system

  106. Konstantinidis, D., Dimitropoulos, K., Daras, P.: Sign language recognition based on hand and body skeletal data. In: 2018-3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4 (2018). IEEE

  107. Karbasi, M., Bhatti, Z., Nooralishahi, P., Shah, A., Mazloomnezhad, S.M.R.: Real-time hands detection in depth image by using distance with kinect camera. Int. J. Internet Things 4(1A), 1–6 (2015)

    Google Scholar 

  108. Bakar, M.Z.A., Samad, R., Pebrianti, D., Aan, N.L.Y.: Real-time rotation invariant hand tracking using 3d data. In: 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), pp. 490–495 (2014). IEEE

  109. Hsieh, C.-C., Liou, D.-H., Lee, D.: A real time hand gesture recognition system using motion history image. In: 2010 2nd International Conference on Signal Processing Systems, vol. 2, pp. 2–394 (2010). IEEE

  110. Van den Bergh, M., Van Gool, L.: Combining rgb and tof cameras for real-time 3d hand gesture interaction. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 66–72 (2011). IEEE

  111. Van den Bergh, M., Koller-Meier, E., Bosché, F., Van Gool, L.: Haarlet-based hand gesture recognition for 3d interaction. In: 2009 Workshop on Applications of Computer Vision (WACV), pp. 1–8 (2009). IEEE

  112. 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, pp. 33–38 (2017)

  113. Maghoumi, M., LaViola, J.J.: Deepgru: Deep gesture recognition utility. In: International Symposium on Visual Computing, pp. 16–31 (2019). Springer

  114. Nunez, J.C., Cabido, R., Pantrigo, J.J., Montemayor, A.S., Velez, J.F.: Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recognit. 76, 80–94 (2018)

    Article  Google Scholar 

  115. Chen, Y., Zhao, L., Peng, X., Yuan, J., Metaxas, D.N.: Construct dynamic graphs for hand gesture recognition via spatial-temporal attention. arXiv preprint arXiv:1907.08871 (2019)

  116. De Smedt, Q., Wannous, H., Vandeborre, J.-P.: Heterogeneous hand gesture recognition using 3d dynamic skeletal data. Comput. Vis. Image Understand. 181, 60–72 (2019)

    Article  Google Scholar 

  117. Bao, P., Maqueda, A.I., del-Blanco, C.R., García, N.: Tiny hand gesture recognition without localization via a deep convolutional network. IEEE Trans. on Consum. Electron. 63(3), 251–257 (2017)

    Article  Google Scholar 

  118. Li, G., Tang, H., Sun, Y., Kong, J., Jiang, G., Jiang, D., Tao, B., Xu, S., Liu, H.: Hand gesture recognition based on convolution neural network. Cluster Comput. 22(2), 2719–2729 (2019)

    Article  Google Scholar 

  119. Garcia-Hernando, G., Yuan, S., Baek, S., Kim, T.-K.: First-person hand action benchmark with rgb-d videos and 3d hand pose annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–419 (2018)

  120. Kim, T.-K., Wong, S.-F., Cipolla, R.: Tensor canonical correlation analysis for action classification. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007). IEEE

  121. Caputo, F.M., Prebianca, P., Carcangiu, A., Spano, L.D., Giachetti, A.: A 3 cent recognizer: Simple and effective retrieval and classification of mid-air gestures from single 3d traces. In: STAG, pp. 9–15 (2017)

  122. Shen, X., Hua, G., Williams, L., Wu, Y.: Dynamic hand gesture recognition: An exemplar-based approach from motion divergence fields. Image Vis. Comput. 30(3), 227–235 (2012)

    Article  Google Scholar 

  123. Liu, L., Shao, L.: Learning discriminative representations from rgb-d video data. In: Twenty-third International Joint Conference on Artificial Intelligence (2013)

  124. Wang, C., Liu, Z., Chan, S.-C.: Superpixel-based hand gesture recognition with kinect depth camera. IEEE Trans. Multimed. 17(1), 29–39 (2014)

    Article  Google Scholar 

  125. Lu, W., Tong, Z., Chu, J.: Dynamic hand gesture recognition with leap motion controller. IEEE Signal Process. Lett. 23(9), 1188–1192 (2016)

    Article  Google Scholar 

  126. Evangelidis, G., Singh, G., Horaud, R.: Skeletal quads: Human action recognition using joint quadruples. In: 2014 22nd International Conference on Pattern Recognition, pp. 4513–4518 (2014). IEEE

  127. Devanne, M., Wannous, H., Berretti, S., Pala, P., Daoudi, M., Del Bimbo, A.: 3-d human action recognition by shape analysis of motion trajectories on Riemannian manifold. IEEE Trans. Cybernet. 45(7), 1340–1352 (2014)

    Article  Google Scholar 

  128. Ohn-Bar, E., Trivedi, M.: Joint angles similarities and hog2 for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 465–470 (2013)

  129. Oreifej, O., Liu, Z.: Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 716–723 (2013)

  130. Narayana, P., Beveridge, R., Draper, B.A.: Gesture recognition: Focus on the hands. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5235–5244 (2018)

  131. Kopuklu, O., Kose, N., Rigoll, G.: Motion fused frames: Data level fusion strategy for hand gesture recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2103–2111 (2018)

  132. Murthy, G., Jadon, R.: A review of vision based hand gestures recognition. Int. J. Inf. Technol. Knowl. Manag. 2(2), 405–410 (2009)

    Google Scholar 

  133. Zhang, T., Lin, H., Ju, Z., Yang, C.: Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models. Int. J. Fuzzy Syst. 22(4), 1330–1341 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the Cyber Forensic and Malware Analysis Lab, Department of Information Technology, Delhi Technological University, New Delhi, India, for providing me necessary resources to carry out the research.

Funding

We did not received any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bindu Verma.

Ethics declarations

Conflict of interest

We declare that we have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tripathi, R., Verma, B. Survey on vision-based dynamic hand gesture recognition. Vis Comput (2023). https://doi.org/10.1007/s00371-023-03160-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00371-023-03160-x

Keywords

Navigation