Skip to main content
SpringerLink
Log in

Computer Vision Techniques for Hand Gesture Recognition: Survey

  • Conference paper
  • First Online:
New Trends in Information and Communications Technology Applications (NTICT 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1764))

Abstract

Hand gesture recognition has recently emerged as a critical component of the human-computer interaction (HCI) concept, allowing computers to capture and interpret hand gestures. In addition to their use in many medical applications, communication between the hearing impaired, device automation, and robot control, hand gestures are of particular importance as a form of nonverbal communication. So far, hand gesture recognition has taken two approaches and relied on a variety of technologies; the first on sensor technology and the second on computer vision. Given the importance of hand gesture recognition applications and technology development today, the importance of the research lies in shedding light on the latest techniques used in the recognition and interpretation of hand gestures. A survey on the techniques used from 2017–2022 has been presented in this research, with a focus on the computer vision approach. The survey was carried out as follows: the first part dealt with research based on artificial intelligence techniques for hand gesture recognition, and the second part focused on research that used artificial neural networks and deep learning for hand gesture recognition.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Yasen, M., Jusoh, S.: A systematic review on hand gesture recognition techniques challenges and applications. Peerj Comput. Sci. 5, e218 (2019)

    Article  Google Scholar 

  3. Camgoz, N.C.: Sign language transformers: joint end-to-end sign language recognition and translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision And Pattern Recognition, pp. 10023–10033 (2020).‏

    Google Scholar 

  4. Tian, L.: Video big data in smart city: background construction and optimization for surveillance video processing. Future Gener. Comput. Syst. 86, pp. 1371–1382 (2018).

    Google Scholar 

  5. Mei, T., Zhang, C.: Deep learning for intelligent video analysis. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1955–1956 (2017).

    Google Scholar 

  6. Sarma, D., Bhuyan, M.K.: Methods, databases and recent advancement of vision-based hand gesture recognition for HCI systems: a review. SN Comput. Sci. 2(6), 1–40 (2021)

    Google Scholar 

  7. Alsaffar, M., et al.: Human-computer interaction using manual hand gestures in real time. Comput. Intell. Neurosci. (2021)

    Google Scholar 

  8. Aumgartl, H., Sauter, D., Schenk, C., Atik, C., Buettner, R.: Vision-based hand gesture recognition for human-computer interaction using MobileNetV2. In: IEEE 45th Annual Computers. Software, and Applications Conference (COMPSAC), pp. 3–15. IEEE, Spain (2018)

    Google Scholar 

  9. Noroozi, F.: Survey on emotional body gesture recognition. IEEE Trans. Affect. Comput. 12(2), 505–523 (2018)

    Google Scholar 

  10. Hisham, B., Hamouda, A.: Supervised learning classifiers for arabic gestures recognition using kinect V2. Appl. Sci. 1(7), 1–21 (2019)

    Google Scholar 

  11. Premaratne, P., Nguyen, Q., Premaratne, M.: Human computer interaction using hand gestures. In: Huang, D.-S., McGinnity, M., Heutte, L., Zhang, X.-P. (eds.) ICIC 2010. CCIS, vol. 93, pp. 381–386. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14831-6_51

    Chapter  Google Scholar 

  12. Thórisson, K.R.: Simulated perceptual grouping: an application to human-computer interaction. In: Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, pp. 876–881. Routledge (2019)

    Google Scholar 

  13. Yun, Y., Ma, D., Yang, M.: Human–computer interaction-based decision support system with applications in data mining. Futur. Gener. Comput. Syst. 114, 285–289 (2021)

    Article  Google Scholar 

  14. Yuanyuan, S.H.I., Yunan, L.I., Xiaolong, F.U., Kaibin, M.I.A.O., Qiguang, M.I.A.O.: Review of dynamic gesture recognition. Virtual Reality Intell. Hardware 3(3), 183–206 (2021)

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Ahmed, M.A.: A review on systems-based sensory gloves for sign language recognition state of the art between 2007 and 2017. Sensors 18(7), 2208 (2018)

    Article  Google Scholar 

  17. Escalera, S., Athitsos, V., Guyon, I.: Challenges in multi-modal gesture recognition. Gesture Recogn., 1–60 (2017)‏

    Google Scholar 

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

    Google Scholar 

  19. Wang, P.: RGB-D-based human motion recognition with deep learning: a survey. Comput. Vis. Image Underst. 171(118), 139 (2018)

    Google Scholar 

  20. Chalasani, T., Smolic, A.: Simultaneous segmentation and recognition: towards more accurate ego gesture recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, p. 0. IEEE (2019)‏

    Google Scholar 

  21. Hernández, I.: Automatic Irish Sign Language Recognition. University of Dublin, Trinity College (2018)

    Google Scholar 

  22. Shaik, K.B.: Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Procedia Comput. Sci. 57(41), 48 (2017)‏

    Google Scholar 

  23. Biswas, A.: Finger detection for hand gesture recognition using circular hough transform. In: Advances in Communication. Devices and Networking, pp. 651–660. Springer, Singapore (2018)

    Google Scholar 

  24. Perimal, M., Basah, S.N., Safar, M.J.A., Yazid, H.: Hand-gesture recognition-algorithm based on finger counting. J. Telecommun. Electron Comput. 10(19), 24 (2018)

    Google Scholar 

  25. Huang, H.: Hand gesture recognition with skin detection and deep learning method. J. Phys. Conf. Ser. 1213(2), 022001 (2019)‏

    Google Scholar 

  26. ljawaryy, A., Malallah, L.: Real-time numerical 0–5 counting based on hand-finger gestures recognition. J. Theor. Appl. Inf. Technol. 95(13), 34–41 (2017)

    Google Scholar 

  27. Prakash, J., Gautam, U.K.: Hand gesture recognition. Int. J. Recent Technol. Eng. (7), 54–59 (2019)

    Google Scholar 

  28. Zhang, F., et al.: Mediapipe hands: on-device real-time hand tracking. arXiv preprint arXiv (2006). 10214 (2020)‏

    Google Scholar 

  29. Karbasi, M., et al.: A hybrid method using Kinect depth and color data stream for hand blobs segmentation. Sci. Int. 71, 515–519 (2017)

    Google Scholar 

  30. Ma, X., Peng, J.: Kinect sensor-based long-distance hand gesture recognition and fingertip detection with depth information. J. Sens. (2018)

    Google Scholar 

  31. Kim, M.S., Lee, C.H.: Hand gesture recognition for Kinect V2 sensor in the near distance where depth data are not provided. Int. J. Softw. Eng. Appl. (10), 407–418 (2016)

    Google Scholar 

  32. Tekin, B., Bogo, F., Pollefeys, M.: Unified Egocentric recognition of 3d hand-object poses and interactions. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 4511–4520. IEEE, USA (2019)

    Google Scholar 

  33. 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, pp. 10853–10862. IEEE, USA (2019)

    Google Scholar 

  34. Ge, L., et al.: 3D hand shape and pose estimation from a single RGB image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10833–10842. IEEE, USA (2019)

    Google Scholar 

  35. Taylor, J., et al.: Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences. ACM Trans. Graph. 35, 1–12 (2017)

    Article  Google Scholar 

  36. Tsoli, A., Argyros, A. A.: Joint 3D tracking of a deformable object in interaction with a hand. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 504–520. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_30

    Chapter  Google Scholar 

  37. Chen, Y., Tu, Z., Ge, L., Zhang, D., Chen, R., So-Handnet, Y.J.: Self-organizing network for 3D hand pose estimation with semi-supervised learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6961–6970. IEEE, Korea (2019)

    Google Scholar 

  38. Ge, L., Ren, Z., Yuan, J.: Point-to-point regression PointNet for 3D hand pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 489–505. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_29

    Chapter  Google Scholar 

  39. Wu, Xiaokun, Finnegan, Daniel, O’Neill, Eamonn, Yang, Yong-Liang.: HandMap: robust hand pose estimation via intermediate dense guidance map supervision. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 246–262. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_15

    Chapter  Google Scholar 

  40. Cai, Y., Ge, L., Cai, J., Yuan, J.: Weakly-supervised 3D hand pose estimation from monocular RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 678–694. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_41

    Chapter  Google Scholar 

  41. Ahmed, K.H., Al-Asadi, A.H.: Survey of hand gesture recognition systems. J. Phys. Conf. Ser., 042003(2019)‏

    Google Scholar 

  42. Abdul, R., Hafız, M.: CMSWVHG-control Ms windows via hand gesture. In: 2017 International Multi-Topic Conference (INMIC), pp. 1–7. IEEE. Pakistan (2017)‏

    Google Scholar 

  43. Zhou, W.: Real-time implementation of vision-based unmarked static hand gesture recognition with neural networks based on FPGAS. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1026–103. IEEE, Macao (2017)‏

    Google Scholar 

  44. Liu, X.., Li, C.: Tian, L.: Hand gesture recognition based on wavelet invariant moments. In: IEEE International Symposium on Multimedia (ISM), pp. 459–464. IEEE, Taiwan (2017)

    Google Scholar 

  45. Salunke, T.P., Bharkad, S.D.: Power point control using hand gesture recognition based on hog feature extraction and K-NN classification. In: International Conference on Computing Methodologies and Communication (ICCMC), pp. 1151–1155. IEEE, India (2017)‏

    Google Scholar 

  46. Saha, H.N., Tapadar, S., Ray, S., Chatterjee, S.K., Saha, S.: A machine learning based approach for hand gesture recognition using distinctive feature extraction. In: 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 91–98. IEEE, USA (2018)

    Google Scholar 

  47. Zhang, Q.: Segmentation of hand gesture based on dark channel prior in projector-camera system. In: International Conference on Communications in China (ICCC), pp. 1–6. IEEE, China (2017)‏

    Google Scholar 

  48. Žemgulys, J.: Recognition of basketball referee signals from videos using histogram of oriented gradients (Hog) and support vector machine (Svm). Procedia Comput. Sci. 130, 953–960 (2018)

    Article  Google Scholar 

  49. Al-Hammadi, M.: Hand gesture recognition for sign language using 3DCNN. IEEE Access 8, 79491–79509 (2020)

    Article  Google Scholar 

  50. Bulugu, I., Ye, Zhongfu., B.J.: Higher-order local autocorrelation feature extraction methodology for hand gestures recognition. In: 2nd International Conference on Multimedia And Image Processing (ICMIP), pp. 83–87. IEEE, China (2017)‏

    Google Scholar 

  51. Ansar, H., Jalal, A., Gochoo, M., Kim, K.: Hand gesture recognition based on auto-landmark localization and reweighted genetic algorithm for healthcare muscle activities. Sustainability 13(5), 2961 (2021)‏

    Google Scholar 

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

    Google Scholar 

  53. Molina, J., Pajuelo, J.A., Martínez, J.M.: Real-time motion-based hand gestures recognition from time-of-flight video. J. Sig. Process Syst. 86, 17–25 (2017)

    Article  Google Scholar 

  54. 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, pp. 37–42. Tokyo, Japan (2018)

    Google Scholar 

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

    Google Scholar 

  56. Mujahid, A., et al.: Real-time hand gesture recognition based on deep learning YOLOv3 model. Appl. Sci. 11(9), 4164 (2021)

    Article  Google Scholar 

  57. Mahmood, M.R., Abdulazeez, A.M., Orman, Z.: Dynamic hand gesture recognition system for Kurdish sign language using two lines of features. In: International Conference on Advanced Science and Engineering (ICOASE), pp. 42–47. IEEE, Iraq (2018)‏

    Google Scholar 

  58. Ganokratanaa, T., Pumrin, S.: Hand gesture recognition algorithm for smart cities based on wireless sensor. Int. J. Online Eng. 13(6), 58–75 (2017)

    Article  Google Scholar 

  59. Augustauskas, R,. Lipnickas, A.: Robust hand detection using arm segmentation from depth data and static palm gesture recognition. In: 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), pp. 664–667. IEEE, Romania (2017)‏

    Google Scholar 

  60. Alksasbeh, M.Z., et al.: Smart hand gestures recognition using K-NN based algorithm for video annotation purposes. Indonesia J. Electron. Eng. Comput. Sci. 21(1), 242–252 (2021)

    Article  Google Scholar 

  61. Liao, S., et al.: Multi-object intergroup gesture recognition combined with fusion feature and KNN algorithm. J. Intell. Fuzzy Syst. 38(3), 2725–2735 (2020)

    Article  Google Scholar 

  62. Benalcázar, M.E., Motoche, C., Zea, J.A., Jaramillo, A.G.: Real-time hand gesture recognition using the MYO armband and muscle activity detection. In: Second Ecuador Technical Chapters Meeting ETCM, pp. 1–6. IEEE, Ecuador (2017)

    Google Scholar 

  63. Lian, K.Y., Chiu, C.C., Hong, Y.J., Sung, W.T.: Wearable armband for real time hand gesture recognition. In: International Conference on Systems Man and Cybernetics (SMC), pp. 2992–2995. IEEE, Canada (2017)

    Google Scholar 

  64. Zhu, Y., Jiang, S., Shull, B.: Wrist-worn hand gesture recognition based on barometric pressure sensing. In: 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 181–184.‏ IEEE, USA (2018)

    Google Scholar 

  65. Sugiura, Y., Nakamura, F., Kawai, W., Kikuchi, T., Sugimoto, M.: Behind the palm: hand gesture recognition through measuring skin deformation on back of hand by using optical sensors. In: 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 1082–1087. IEEE, Japan (2017)‏‏

    Google Scholar 

  66. Reshna, S., Jayaraju, M.: Spotting and recognition of hand gesture for Indian sign language recognition system with skin segmentation and SVM. In: International Conference on Wireless Communications, Signal Processing and Networking (WISPNET), pp. 386–390.‏ IEEE, Indian (2017)

    Google Scholar 

  67. Tian, Z.: WICATCH: a Wi-Fi based hand gesture recognition system. IEEE Access (6), 16911–16923 (2018)‏

    Google Scholar 

  68. Sapienza, S.: On-line event-driven hand gesture recognition based on surface electromyographic signals. In: International Symposium on Circuits and Systems (ISCAS), pp. 1–5.‏ IEEE, Italy (2018)

    Google Scholar 

  69. Shengchang, L., Haoyu, T., Wenshuang, Y.: A hand gesture recognition system based on 24 Ghz radars. In: International Symposium on Antennas and Propagation ISAP, pp. 1–2. IEEE, India (2017)

    Google Scholar 

  70. Zhao, J., Mao, J., Wang, G., Yang, H., Zhao, B.: A miniaturized wearable wireless hand gesture recognition system employing deep-forest classifier. In: Biomedical Circuits and Systems Conference, pp. 1–4. IEEE, Italy (2017)

    Google Scholar 

  71. Rishabh, S., Nutan, V., Prachi, R.: Interactive projector screen with hand detection using gestures. In: International Conference on Automatic Control and Dynamic Optimization Techniques ICACDOT, pp. 574–577. IEEE, India (2016)

    Google Scholar 

  72. Yang, J., Pan, J., Li, J.: sEMG-based continuous hand gesture recognition using Gmm-Hmm and threshold model. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1509–1514. IEEE, Macao (2017)‏

    Google Scholar 

  73. Chen, S.H., Hernawan, A., Lee, Y.S., Wang, J.C.: Hand gesture recognition based on Bayesian sensing hidden Markov models and Bhattacharyya divergence. In: International Conference on Image Processing (ICIP), pp. 3535–3539. IEEE, China (2017)

    Google Scholar 

  74. Molina, J., Pajuelo, J.A., Martínez, J.M.: Real-time motion-based hand gestures recognition from time-of-flight video. J. Sig. Process. Syst. 86(1), 17–25 (2017)

    Google Scholar 

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

    Google Scholar 

  76. De Smedt, Q., Wannous, H., Vandeborre, J.P., Guerry, J., Saux, B.: 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. Eurographics Association, Germany ‏(2017)

    Google Scholar 

  77. Karbasi, M.: A hybrid method using Kinect depth and color data stream for hand blobs segmentation. Sci. Int. 29, 515–519 (2017)

    Google Scholar 

  78. Guo, Y., He, Z., Xie, Q., Chen, K., Ni, W., Zou, E.: Development and application of gesture recognition system for intelligent robot. IOP Conf. Ser. Mater. Sci. Eng. 452(042172) (2018)‏

    Google Scholar 

  79. Oliveira, M., Chatbri, H., Yarlapati, N., O’Connor, N. E., Sutherland, A.: Hand orientation redundancy filter applied to hand-shapes dataset. In: Proceedings of the 2nd International Conference on Applications of Intelligent Systems, pp. 1–5. Association for Computing Machinery, USA (2019)‏

    Google Scholar 

  80. De Smedt, Q., Wannous, H., Vandeborre, P.: Heterogeneous hand gesture recognition using 3D dynamic skeletal data. Comput. Visi. Image Underst. 181, 60–72 (2019)

    Article  Google Scholar 

  81. Oliveira, M., Chatbri, H., Little, S., Ferstl, Y., O’Connor, N. E., Sutherland, A.: Irish sign language recognition using principal component analysis and convolutional neural networks. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE, Australia (2017)

    Google Scholar 

  82. Benitez, G., Olivares, J., Sanchez, G., Yanai, K.: IPN hand: a video dataset and benchmark for real-time continuous hand gesture recognition. In: 25th International Conference on Pattern Recognition (ICPR), pp. 4340–4347. IEEE, Macao (2021)

    Google Scholar 

  83. Malik, J., Elhayek, A., Stricker, D.: Structure-aware 3D hand pose regression from a single depth image. In: Bourdot, P., Cobb, S., Interrante, V., kato, H., Stricker, D. (eds.) EuroVR 2018. LNCS, vol. 11162, pp. 3–17. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01790-3_1

    Chapter  Google Scholar 

  84. Chen, J., Meng, J., Wang, X., Yuan, J.: Dynamic graph CNN for event-camera based gesture recognition. In: International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE, India ‏(2020)

    Google Scholar 

  85. Sun, H., Ji, T., Zhang, B., Yang, K., Ji, R.: Research on the hand gesture recognition based on deep learning. In: 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE), pp. 1–4. IEEE, China (2018)

    Google Scholar 

  86. Shin, S., Sung, W.: Dynamic hand gesture recognition for wearable devices with low complexity recurrent neural networks. In: International Symposium on Circuits and Systems (ISCAS), pp. 2274–2277. IEEE, Canada (2020)‏

    Google Scholar 

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

    Google Scholar 

  88. lnujaim, I., Alali, H., Khan, F., Kim, Y.: Hand gesture recognition using input impedance variation of two antennas with transfer learning. IEEE Sens. J., 4129–4135 (2018)

    Google Scholar 

  89. Nguyen, 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 Conference on Computer Vision and Pattern Recognition, pp. 12036–12045. IEEE, USA (2019)

    Google Scholar 

  90. Alnaim, N., Abbod, M., Albar, A.: Hand gesture recognition using convolutional neural network for people who have experienced a stroke. In: Proceedings of the 3rd international symposium on multidisciplinary studies and innovative technologies (ISMSIT), pp.1–6. Ankara, Turkey (2019)

    Google Scholar 

  91. Chung, H., Chung, Y., Tsai, W.: An efficient hand gesture recognition system based on deep CNN. In: Proceedings of the International Conference on Industrial Technology (ICIT), pp. 853–858. IEEE, Australia (2019)

    Google Scholar 

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

    Article  Google Scholar 

  93. Li, G., et al.: Hand gesture recognition based on convolution neural network. Cluster Comput. 22(2), 2719–2729 (2019)

    Article  Google Scholar 

  94. Wu, X.Y.: A hand gesture recognition algorithm based on Dc-CNN. Multimedia Tools Appl. 79(13), 1–13 (2019)

    Google Scholar 

  95. Lai, K., Yanushkevich, S.N.: CNN+RNN depth and skeleton based dynamic hand gesture recognition. In: International Conference on Pattern Recognition (ICPR), pp. 3451–3456. IEEE.‏ China (2018)

    Google Scholar 

  96. Choi, W., Ryu, S., Kim, J.: Short-range radar based real-time hand gesture recognition using Lstm encoder. IEEE Access 7 (2019)

    Google Scholar 

  97. 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 Recogn. 76, 80–94 (2018)

    Article  Google Scholar 

  98. Zhu, G., Zhang, L., Shen, P., Song, J., Shah, S.A., Bennamoun, M.: Continuous gesture segmentation and recognition using 3DCNN and convolutional LSTM. IEEE Trans. Multimedia 21(4), 1011–1021 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software Department, University of Babylon, Babylon, Iraq

    Noor Fadel

  2. Computer Science Department, Mustansiriyah University, Baghdad, Iraq

    Emad I. Abdul Kareem

Authors
  1. Noor Fadel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emad I. Abdul Kareem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Noor Fadel .

Editor information

Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Safaa O. Al-Mamory

  3. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  4. University of Information Technology and Communications, Baghdad, Iraq

    Loay E. George

  5. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. Open University of Tanzania, Dar es Salaam, Tanzania

    George S. Oreku

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fadel, N., Kareem, E.I.A. (2023). Computer Vision Techniques for Hand Gesture Recognition: Survey. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2022. Communications in Computer and Information Science, vol 1764. Springer, Cham. https://doi.org/10.1007/978-3-031-35442-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35442-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35441-0

  • Online ISBN: 978-3-031-35442-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation