Skip to main content
SpringerLink
Log in
Book cover

European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 697–714Cite as

Fully Convolutional Networks for Continuous Sign Language Recognition

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12369))

Abstract

Continuous sign language recognition (SLR) is a challenging task that requires learning on both spatial and temporal dimensions of signing frame sequences. Most recent work accomplishes this by using CNN and RNN hybrid networks. However, training these networks is generally non-trivial, and most of them fail in learning unseen sequence patterns, causing an unsatisfactory performance for online recognition. In this paper, we propose a fully convolutional network (FCN) for online SLR to concurrently learn spatial and temporal features from weakly annotated video sequences with only sentence-level annotations given. A gloss feature enhancement (GFE) module is introduced in the proposed network to enforce better sequence alignment learning. The proposed network is end-to-end trainable without any pre-training. We conduct experiments on two large scale SLR datasets. Experiments show that our method for continuous SLR is effective and performs well in online recognition.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Camgoz, N., Hadfield, S., Koller, O., Ney, H., Bowden, R.: Neural sign language translation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 7784–7793 (2018)

    Google Scholar 

  2. Camgoz, N.C., Hadfield, S., Koller, O., Bowden, R.: Subunets: end-to-end hand shape and continuous sign language recognition. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3075–3084 (2017)

    Google Scholar 

  3. Cooper, H., Bowden, R.: Learning signs from subtitles: a weakly supervised approach to sign language recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2568–2574 (2009)

    Google Scholar 

  4. Cui, R., Liu, H., Zhang, C.: Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1610–1618 (2017)

    Google Scholar 

  5. Cui, R., Liu, H., Zhang, C.: A deep neural framework for continuous sign language recognition by iterative training. IEEE Trans. Multimedia 21, 1880–1891 (2019)

    Article  Google Scholar 

  6. Evangelidis, G.D., Singh, G., Horaud, R.: Continuous gesture recognition from articulated poses. In: Proceedings of European Conference on Computer Vision, pp. 595–607 (2015)

    Google Scholar 

  7. Fang, G., Gao, W.: A SRN/HMM system for signer-independent continuous sign language recognition. In: Proceedings of IEEE International Conference on Automatic Face Gesture Recognition, pp. 312–317 (2002)

    Google Scholar 

  8. Farhadi, A., Forsyth, D.: Aligning ASL for statistical translation using a discriminative word model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1471–1476 (2006)

    Google Scholar 

  9. Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of International Conference on Machine Learning, pp. 369–376 (2006)

    Google Scholar 

  10. Guo, D., Zhou, W., Li, H., Wang, M.: Online early-late fusion based on adaptive HMM for sign language recognition. ACM Trans. Multimedia Comput. Communi. Appl. 14, 1–18 (2017)

    Google Scholar 

  11. Guo, D., Zhou, W., Li, H., Wang, M.: Hierarchical LSTM for sign language translation. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 6845–6852 (2018)

    Google Scholar 

  12. Guo, D., Zhou, W., Wang, M., Li, H.: Sign language recognition based on adaptive HMMs with data augmentation. In: Proceedings of IEEE International Conference on Image Processing, pp. 2876–2880 (2016)

    Google Scholar 

  13. Han, J., Awad, G., Sutherland, A.: Modelling and segmenting subunits for sign language recognition based on hand motion analysis. Pattern Recogn. Lett. 30, 623–633 (2009)

    Article  Google Scholar 

  14. Huang, J., Zhou, W., Zhang, Q., Li, H., Li, W.: Video-based sign language recognition without temporal segmentation. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 2257–2264 (2018)

    Google Scholar 

  15. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of International Conference on Machine Learning, pp. 448–456 (2015)

    Google Scholar 

  16. Kelly, D., McDonald, J., Markham, C.: Recognizing spatiotemporal gestures and movement epenthesis in sign language. In: Proceedings of IEEE International Conference on Image Processing and Machine Vision, pp. 145–150 (2009)

    Google Scholar 

  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR preprint CoRR:1412.6980 (2014)

    Google Scholar 

  18. Koller, O., Forster, J., Ney, H.: Continuous sign language recognition: towards large vocabulary statistical recognition systems handling multiple signers. Comput. Vis. Image Underst. 141, 108–125 (2015)

    Article  Google Scholar 

  19. Koller, O., Ney, H., Bowden, R.: Deep hand: how to train a CNN on 1 million hand images when your data is continuous and weakly labelled. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3793–3802 (2016)

    Google Scholar 

  20. Koller, O., Zargaran, S., Ney, H.: Re-sign: re-aligned end-to-end sequence modelling with deep recurrent CNN-HMMs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3416–3424 (2017)

    Google Scholar 

  21. Koller, O., Zargaran, S., Ney, H., Bowden, R.: Deep sign: hybrid CNN-HMM for continuous sign language recognition. In: Proceedings of British Machine Vision Conference, pp. 136.1–136.12 (2016)

    Google Scholar 

  22. Liddell, S.K.: Grammar, Gestures, and Meaning in American Sign Language, pp. 52–53. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  23. Liwicki, M., Graves, A., Bunke, H., Schmidhuber, J.: A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. In: Proceedings of International Conference on Document Analysis and Recognition, pp. 367–371 (2007)

    Google Scholar 

  24. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  25. Miao, Y., Gowayyed, M., Metze, F.: Eesen: end-to-end speech recognition using deep RNN models and WFST-based decoding. In: IEEE Conference on Automatic Speech Recognition and Understanding Workshops, pp. 167–174 (2015)

    Google Scholar 

  26. 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 networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4207–4215 (2016)

    Google Scholar 

  27. Ong, S., Ranganath, S.: Automatic sign language analysis: a survey and the future beyond lexical meaning. IEEE Trans. Pattern Anal. Mach. Intell. 27, 873–91 (2005)

    Article  Google Scholar 

  28. Pan, Y., Mei, T., Yao, T., Li, H., Rui, Y.: Jointly modeling embedding and translation to bridge video and language. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4594–4602 (2015)

    Google Scholar 

  29. Pitsikalis, V., Theodorakis, S., Vogler, C., Maragos, P.: Advances in phonetics-based sub-unit modeling for transcription alignment and sign language recognition. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2011)

    Google Scholar 

  30. Pu, J., Zhou, W., Li, H.: Dilated convolutional network with iterative optimization for continuous sign language recognition. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 885–891 (2018)

    Google Scholar 

  31. Pu, J., Zhou, W., Li, H.: Iterative alignment network for continuous sign language recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4165–4174 (2019)

    Google Scholar 

  32. Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: Proceedings of International Conference on Document Analysis and Recognition, pp. 67–72 (2017)

    Google Scholar 

  33. Sak, H., Senior, A., Rao, K., İrsoy, O., Graves, A., Beaufays, F., Schalkwyk, J.: Learning acoustic frame labeling for speech recognition with recurrent neural networks. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4280–4284 (2015)

    Google Scholar 

  34. Sun, C., Zhang, T., Bao, B.K., Xu, C., Mei, T.: Discriminative exemplar coding for sign language recognition with kinect. IEEE Trans. Cybern. 43, 1418–1428 (2013)

    Article  Google Scholar 

  35. Theodorakis, S., Katsamanis, A., Maragos, P.: Product-HMMs for automatic sign language recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1601–1604 (2009)

    Google Scholar 

  36. Vela, A.H., et al.: Probability-based dynamic time warping and bag-of-visual-and-depth-words for human gesture recognition in RGB-D. Pattern Recogn. Lett. 50, 112–121 (2014)

    Article  Google Scholar 

  37. Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: Sequence to sequence - video to text. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4534–4542 (2015)

    Google Scholar 

  38. Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R., Saenko, K.: Translating videos to natural language using deep recurrent neural networks. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1494–1504 (2015)

    Google Scholar 

  39. Wang, B., Ma, L., Zhang, W., Liu, W.: Reconstruction network for video captioning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 7622–7631 (2018)

    Google Scholar 

  40. Yang, H.D., Lee, S.W.: Robust sign language recognition with hierarchical conditional random fields. In: Proceedings of IEEE International Conference on Pattern Recognition, pp. 2202–2205 (2010)

    Google Scholar 

  41. Yang, R., Sarkar, S.: Detecting coarticulation in sign language using conditional random fields. In: Proceedings of IEEE International Conference on Pattern Recognition, pp. 108–112 (2006)

    Google Scholar 

  42. Yang, R., Sarkar, S.: Gesture recognition using hidden Markov models from fragmented observations. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 766–773 (2006)

    Google Scholar 

  43. Yang, W., Tao, J., Ye, Z.: Continuous sign language recognition using level building based on fast hidden Markov model. Pattern Recogn. Lett. 78, 28–35 (2016)

    Article  Google Scholar 

  44. Yang, Z., Shi, Z., Shen, X., Tai, Y.W.: SF-net: structured feature network for continuous sign language recognition. arXiv preprint arXiv:1908.01341 (2019)

  45. Yao, L., et al.: Describing videos by exploiting temporal structure. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4507–4515 (2015)

    Google Scholar 

  46. Yin, F., Chai, X., Zhou, Y., Chen, X.: Weakly supervised metric learning towards signer adaptation for sign language recognition. In: Proceedings of British Machine Vision Conference, pp. 35.1–35.12 (2015)

    Google Scholar 

  47. Zhang, J., Zhou, W., Li, H.: A threshold-based HMM-DTW approach for continuous sign language recognition. In: Proceedings of International Conference on Internet Multimedia Computing and Service, pp. 237–240 (2014)

    Google Scholar 

  48. Zhang, J., Zhou, W., Xie, C., Pu, J., Li, H.: Chinese sign language recognition with adaptive HMM. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1–6 (2016)

    Google Scholar 

  49. Zhou, H., Zhou, W., Zhou, Y., Li, H.: Spatial-temporal multi-cue network for continuous sign language recognition. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 13009–13016 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Ka Leong Cheng, Qifeng Chen & Yu-Wing Tai

  2. Tencent, Shenzhen, China

    Zhaoyang Yang

  3. Kwai Inc., Beijing, China

    Yu-Wing Tai

Authors
  1. Ka Leong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaoyang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qifeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Wing Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-Wing Tai .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (zip 17871 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheng, K.L., Yang, Z., Chen, Q., Tai, YW. (2020). Fully Convolutional Networks for Continuous Sign Language Recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58586-0_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58585-3

  • Online ISBN: 978-3-030-58586-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation