References

Abreu, J.G., J.M. Teixeira, L.S. Figueiredo and V. Teichrieb, 2016. Evaluating sign language recognition using the Myo armband. Proceedings of the 2016 18th International Symposium on Virtual and Augmented Reality (SVR), June 21-24, 2016, IEEE, Gramado, Brazil, ISBN:978-1-5090-4149-7, pp: 64-70.

Adamo-Villani, N. and R. Wilbur, 2010. Software for Math and Science education for the deaf. Disability Rehabil. Assistive Technol., 5: 115-124.
CrossRef  |  PubMed  |  Direct Link  |  

Adamo-Villani, N. and S. Anasingaraju, 2017. Holographic signing avatars for deaf education. Proceedings of the 3rd International Conference on E-Learning, E-Education and Online Training (eLEOT’16), August 31-September 2, 2016, Dublin, Ireland, pp: 54-61.

Agrawal, S.C., A.S. Jalal and R.K. Tripathi, 2016. A survey on manual and non-manual sign language recognition for isolated and continuous sign. Int. J. Applied Pattern Recognit., 3: 99-134.
CrossRef  |  Direct Link  |  

Aldoukhi, A.H., H. Law, K.M. Black, W.W. Roberts, J. Deng and K.R. Ghani, 2019. PD04-06 Deep learning computer vision algorithm for detecting kidney stone composition: Towards an automated future. J. Urology, 201: e75-e76.
CrossRef  |  Direct Link  |  

Alfonse, M., A. Ali, A.S. Elons, N.L. Badr and M. Aboul-Ela, 2015. Arabic sign language benchmark database for different heterogeneous sensors. Proceedings of the 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA’15), December 21-23, 2015, IEEE, Marrakech, Morocco, pp: 1-9.

Aly, S., A.L. Abbott and M. Torki, 2016. A multi-modal feature fusion framework for kinect-based facial expression recognition using Dual Kernel Discriminant Analysis (DKDA). Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV’16), March 7-10, 2016, IEEE, Lake Placid, New York, USA., pp: 1-10.

Amin, O., H. Said, A. Samy and H.K. Mohammed, 2015. HMM based automatic Arabic sign language translator using Kinect. Proceedings of the 2015 10th International Conference on Computer Engineering & Systems (ICCES’15), December 23-24, 2015, IEEE, Cairo, Egypt, pp: 389-392.

Anil, J. and L.P. Suresh, 2016. Literature survey on face and face expression recognition. Proceedings of the 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT’16), March 18-19, 2016, IEEE, Nagercoil, India, pp: 1-6.

Assaleh, K., T. Shanableh, M. Fanaswala, F. Amin and H. Bajaj, 2010. Continuous Arabic sign language recognition in user dependent mode. J. Intell. Learn. Syst. Appl., 2: 19-27.
CrossRef  |  Direct Link  |  

Bacchi, S., L. Oakden-Rayner, T. Zerner, T. Kleinig, S. Patel and J. Jannes, 2019. Deep learning natural language processing successfully predicts the cerebrovascular cause of transient ischemic attack-like presentations. Stroke, 50: 758-760.
CrossRef  |  PubMed  |  Direct Link  |  

Baranwal, N., K. Tripathi and G.C. Nandi, 2015. Possibility theory based continuous Indian Sign Language gesture recognition. Proceedings of the TENCON 2015 IEEE Region 10 Conference, November 1-4, 2015, IEEE, Macao, China, pp: 1-5.

Bhuyan, M.K., D.A. Kumar, K.F. MacDorman and Y. Iwahori, 2014. A novel set of features for continuous hand gesture recognition. J. Multimodal User Interfaces, 8: 333-343.
CrossRef  |  Direct Link  |  

Brancati, N., G.D. Pietro, M. Frucci and L. Gallo, 2017. Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering. Comput. Vision Image Understanding, 155: 33-42.
CrossRef  |  Direct Link  |  

Brand, M., N. Oliver and A. Pentland, 1997. Coupled hidden Markov models for complex action recognition. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, June 17-19, 1997, IEEE, San Juan, Puerto Rico, USA., pp: 994-999.

Camgoz, N.C., S. Hadfield, O. Koller and R. Bowden, 2017. Subunets: End-to-end hand shape and continuous sign language recognition. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV’17), October 22-29, 2017, IEEE, Venice, Italy, pp: 3075-3084.

Caplier, A., L. Bonnaud, S. Malassiotis and M.G. Strintzis, 2004. Comparison of 2D and 3D analysis for automated cued speech gesture recognition. Proceedings of the 9th International Conference on Speech and Computer (SPECOM’04), September 20-22, 2004, Saint-Petersburg, Russia, pp: 35-41.

Chai, X., G. Li, X. Chen, M. Zhou, G. Wu and H. Li, 2013. Visualcomm: A tool to support communication between deaf and hearing persons with the kinect. Proceedings of the 15th International ACM International SIGACCESS Conference on Computers and Accessibility (ASSETS'13), October 21-23, 2013, ACM, Bellevue, Washington, USA., pp: 1-2.

Chai, X., Z. Liu, F. Yin, Z. Liu and X. Chen, 2016. Two streams recurrent neural networks for large-scale continuous gesture recognition. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR’16), December 4-8, 2016, IEEE, Cancun, Mexico, pp: 31-36.

Chebka, R. and F. Essalmi, 2015. A crosswords game for deaf. Proceedings of the 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA’15), December 21-23, 2015, IEEE, Marrakech, Morocco, pp:1-6.

Chen, L., H. Wei and J. Ferryman, 2013. A survey of human motion analysis using depth imagery. Pattern Recognit. Lett., 34: 1995-2006.
CrossRef  |  Direct Link  |  

Cheng, H., L. Yang and Z. Liu, 2015. Survey on 3D hand gesture recognition. IEEE. Trans. Circuits Syst. Video Technol., 26: 1659-1673.
CrossRef  |  Direct Link  |  

Cheng, J., X. Chen, A. Liu and H. Peng, 2015. A novel phonology-and radical-coded Chinese sign language recognition framework using accelerometer and surface electromyography sensors. Sensors, 15: 23303-23324.
CrossRef  |  PubMed  |  Direct Link  |  

Cheok, M.J., Z. Omar and M.H. Jaward, 2019. A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. Cybern., 10: 131-153.
CrossRef  |  Direct Link  |  

Chossat, J.B., Y. Tao, V. Duchaine and Y.L. Park, 2015. Wearable soft artificial skin for hand motion detection with embedded microfluidic strain sensing. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA’15), May 26-30, 2015, IEEE, Seattle, Washington, USA., pp: 2568-2573.

Chuan, C.H., E. Regina and C. Guardino, 2014. American sign language recognition using leap motion sensor. Proceedings of the 2014 13th International Conference on Machine Learning and Applications, December 3-6, 2014, IEEE, Detroit, Michigan, USA., pp: 541-544.

Corneanu, C.A., M.O. Simon, J.F. Cohn and S.E. Guerrero, 2016. Survey on RGB, 3D, thermal and multimodal approaches for facial expression recognition: History, trends and affect-related applications. IEEE. Trans. Pattern Anal. Mach. Intell., 38: 1548-1568.
CrossRef  |  PubMed  |  Direct Link  |  

Cui, R., H. Liu and C. Zhang, 2017. Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, Hawaii, USA., pp: 7361-7369.

Dreuw, P., C. Neidle, V. Athitsos, S. Sclaroff and H. Ney, 2008. Benchmark databases for video-based automatic sign language recognition. Proceedings of the International Conference on Language Resources and Evaluation (LREC’08), May 26-June 1, 2008, Marrakech, Morocco, pp: 1-7.

Dreuw, P., J. Forster, T. Deselaers and H. Ney, 2008. Efficient approximations to model-based joint tracking and recognition of continuous sign language. Proceedings of the 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, September 17-19, 2008, IEEE, Amsterdam, Netherlands, pp: 1-6.

Ekman, P. and V.W. Friesen, 1978. Facial Action Coding System. Psychologist Press Inc., San Francisco, California.

Escudeiro, P., N. Escudeiro, R. Reis, J. Lopes and M. Norberto et al., 2015. Virtual sign-A real time bidirectional translator of Portuguese sign language. Procedia Comput. Sci., 67: 252-262.
CrossRef  |  Direct Link  |  

Escudeiro, P., N. Escudeiro, R. Reis, M. Barbosa and J. Bidarra et al., 2014. Virtual sign game learning sign language. Proceedings of the 5th International Conference on Computers and Technology in Modern Education, April 23-25, 2014, Renaissance Kuala Lumpur Hotel, Kuala Lumpur, Malaysia, pp: 26-33.

Forster, J., C. Schmidt, T. Hoyoux, O. Koller, U. Zelle, J.H. Piater and H. Ney, 2012. RWTH-PHOENIX-Weather: A Large Vocabulary Sign Language Recognition and Translation Corpus. Proceedings of the International Conference on Language Resources and Evaluation (LREC’12), May 2012, Istanbul, Turkey, pp: 3785-3789.

Forster, J., O. Koller, C. Oberdorfer, Y. Gweth and H. Ney, 2013. Improving continuous sign language recognition: Speech recognition techniques and system design. Proceedings of the 4th International Workshop on Speech and Language Processing for Assistive Technologies, August 2013, Grenoble, France, pp: 41-46.

Gameiro, J., T. Cardoso and Y. Rybarczyk, 2013. Kinect-sign: Teaching sign language to listeners through a game. Procedia Technol., 17: 384-391.
Direct Link  |  

Gao, W., G. Fang, D. Zhao and Y. Chen, 2004. Transition movement models for large vocabulary continuous sign language recognition. Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, May 19, 2004, IEEE, Seoul, South Korea, pp: 553-558.

Garcia, M.G., C.I.S. Luis and M.J.C. Samonte, 2016. E-tutor for Filipino sign language. Proceedings of the 2016 11th International Conference on Computer Science & Education (ICCSE’16), August 23-25, 2016, IEEE, Nagoya, Japan, pp: 223-227.

Ghotkar, A.S. and G.K. Kharate, 2015. Dynamic hand gesture recognition and novel sentence interpretation algorithm for Indian sign language using microsoft kinect sensor. J. Pattern Recognit. Res., 1: 24-38.
Direct Link  |  

Goodfellow, I., Y. Bengio and A. Courville, 2016. Deep Learning. MIT Press, Massachusetts, United States, ISBN: 9780262337373, Pages: 800.

Graves, A., S. FernAndez, F. Gomez and J. Schmidhuber, 2006. Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. Proceedings of the 23rd International Conference on Machine Learning (ICML'06), June 25-29, 2006, ACM, Pittsburgh, Pennsylvania, USA., pp: 369-376.

Gweth, Y.L., C. Plahl and H. Ney, 2012. Enhanced continuous sign language recognition using PCA and neural network features. Proceedings of the 2012 IEEE International Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’12), June 16-21, 2012, IEEE, Providence, Rhode Island, USA., pp: 55-60.

Hochreiter, S. and J. Schmidhuber, 1997. Long short-term memory. Neural Comput., 9: 1735-1780.
CrossRef  |  PubMed  |  Direct Link  |  

Holt, G.A.T., A.J.V. Doorn, M.J.T. Reinders, E.A. Hendriks and H.D. Ridder, 2011. Human-inspired search for redundancy in automatic sign language recognition. ACM. Trans. Applied Percept. (TAP), Vol. 8, No. 2. 10.1145/1870076.1870083

Hoshino, K., 2006. Dexterous robot hand control with data glove by human imitation. IEICE. Trans. Inf. Syst., 89: 1820-1825.
CrossRef  |  Direct Link  |  

Huang, C.L. and B.L. Tsai, 2010. A vision-based Taiwanese sign language recognition. Proceedings of the 2010 20th International Conference on Pattern Recognition, August 23-26, 2010, IEEE, Istanbul, Turkey, pp: 3683-3686.

Huang, J., W. Zhou, Q. Zhang, H. Li and W. Li, 2018. Video-based sign language recognition without temporal segmentation. Proceedings of the 32nd AAAI International Conference on Artificial Intelligence (AAAI’18), February 2-7, 2018, New Orleans, Louisiana, USA., pp: 1-8.

Hyeon-Kyu, L. and J.H. Kim, 1999. An HMM-based threshold model approach for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 21: 961-973.
CrossRef  |  Direct Link  |  

Kakde, M.U., M.G. Nakrani and A.M. Rawate, 2016. A review paper on sign language recognition system for deaf and dumb people using image processing. Int. J. Eng. Res. Technol. (IJERT), 5: 590-592.
Direct Link  |  

Kelly, D., J. McDonald and C. Markham, 2009. Recognizing spatiotemporal gestures and movement epenthesis in sign language. Proceedings of the 2009 13th International Conference on Machine Vision and Image Processing, September 2-4, 2009, IEEE, Dublin, Ireland, pp: 145-150.

Kelly, D., J. McDonald and C. Markham, 2010. A person independent system for recognition of hand postures used in sign language. Pattern Recog. Lett., 31: 1359-1368.
CrossRef  |  Direct Link  |  

Kelly, D., J. McDonald and C. Markham, 2011. Recognition of Spatiotemporal Gestures in Sign Language Using Gesture Threshold Hmms. In: Machine Learning for Vision-Based Motion Analysis, Wang L., G. Zhao, L. Cheng and M. Pietikainen (Eds.). Springer, London, UK., ISBN: 978-0-85729-056-4, pp: 307-348.

Kelly, D., J.R. Delannoy, J. McDonald and C. Markham, 2009. A framework for continuous multimodal sign language recognition. Proceedings of the 2009 International Conference on Multimodal Interfaces (ICMI-MLMI'09), November 02-04, 2009, ACM, Cambridge, Massachusetts, USA., pp: 351-358.

Khan, S., D.G. Bailey and G.S. Gupta, 2014. Pause detection in continuous sign language. Int. J. Comput. Appl. Technol., 50: 75-83.
CrossRef  |  Direct Link  |  

Kishore, P.V.V., M.V.D. Prasad, D.A. Kumar and A.S.C.S. Sastry, 2016. Optical flow hand tracking and active contour hand shape features for continuous sign language recognition with artificial neural networks. Proceedings of the 2016 IEEE 6th International Conference on Advanced Computing (IACC’16), February 27-28, 2016, IEEE, Bhimavaram, India, pp: 346-351.

Klakow, D. and J. Peters, 2002. Testing the correlation of word error rate and perplexity. Speech Commun., 38: 19-28.
CrossRef  |  Direct Link  |  

Koller, O., C. Camgoz, H. Ney and R. Bowden, 2019. Weakly supervised learning with multi-stream CNN-LSTM-HMMs to discover sequential Parallelism in sign language videos. IEEE. Trans. Pattern Anal. Mach. Intell., Vol. 2019, 10.1109/TPAMI.2019.2911077

Koller, O., J. Forster and H. Ney, 2015. Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers. Comput. Vision Image Understanding, 141: 108-125.
CrossRef  |  Direct Link  |  

Koller, O., O. Zargaran, H. Ney and R. Bowden, 2016. Deep sign: Hybrid CNN-HMM for continuous sign language recognition. Proceedings of the British Conference on Machine Vision (BMVC’16), September 19-22, 2016, New York, USA., pp: 1-12.

Koller, O., S. Zargaran and H. Ney, 2017. Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent CNN-HMMs. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, IEEE, Honolulu, Hawaii, pp: 4297-4305.

Kong, W.W. and S. Ranganath, 2014. Towards subject independent continuous sign language recognition: A segment and merge approach. Pattern Recognit., 47: 1294-1308.
CrossRef  |  Direct Link  |  

Li, K., Z. Zhou and C.H. Lee, 2016. Sign transition modeling and a scalable solution to continuous sign language recognition for real-world applications. ACM. Trans. Accessible Comput. (TACCESS), Vol. 8, No.2. 10.1145/2850421

Li, Y., X. Chen, J. Tian, X. Zhang, K. Wang and J. Yang, 2010. Automatic recognition of sign language subwords based on portable accelerometer and EMG sensors. Proceedings of the International Joint Conference and Workshop on Multimodal Interfaces and the Machine Learning for Multimodal Interaction (ICMI-MLMI'10), November 08-10, 2010, ACM, Beijing, China, pp: 1-17.

Li, Y., X. Chen, X. Zhang, K. Wang and J. Yang, 2011. Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 30-September 3, 2011, IEEE, Boston, Massachusetts, USA., pp: 3358-3361.

Li, Y., X. Chen, X. Zhang, K. Wang and Z.J. Wang, 2012. A sign-component-based framework for Chinese sign language recognition using accelerometer and sEMG data. IEEE. Trans. Biomed. Eng., 59: 2695-2704.
CrossRef  |  PubMed  |  Direct Link  |  

Lichtenaur, J.F., E.A. Hendriks and M.J.T. Reinders, 2008. Sign language recognition by combining statistical DTW and independent classification. IEEE. trans. pattern anal. machine intell., 30: 2040-2046.
Direct Link  |  

Lotfi, E., B. Amine and B. Mohammed, 2015. Teaching Arabic sign language through an interactive web based serious game. Int. J. Comput. Appl., 116: 12-18.
CrossRef  |  Direct Link  |  

Lu, J., V. Behbood, P. Hao, H. Zuo, S. Xue and G. Zhang, 2015. Transfer learning using computational intelligence: A survey. Knowl. Based Syst., 80: 14-23.
CrossRef  |  Direct Link  |  

Lun, R. and W. Zhao, 2015. A survey of applications and human motion recognition with microsoft kinect. Int. J. Pattern Recognit. Artif. Intell., Vol. 29, No. 05. 10.1142/S0218001415550083

Maraqa, M., F. Al-Zboun, M. Dhyabat and R.A. Zitar, 2012. Recognition of Arabic Sign Language (ArSL) using recurrent neural networks. J. Intell. Learning Syst. Appl., 4: 41-52.
Direct Link  |  

Marin, G., F. Dominio and P. Zanuttigh, 2014. Hand gesture recognition with leap motion and kinect devices. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), October 27-30, 2014, IEEE, Paris, France, pp: 1565-1569.

Martinez, A., B. Wilbur, R. Shay and A. Kak, 2002. Purdue RVL-SLLL ASL database for automatic recognition of American Sign Language. Proceedings of the 4th IEEE International Conference on Multimodal Interfaces, October 14-16, 2002, IEEE Computer Society Washington, DC., USA., pp: 167-172.

Martinez, B. and M.F. Valstar, 2016. Advances, Challenges and Opportunities in Automatic Facial Expression Recognition. In: Advances in Face Detection and Facial Image Analysis, Kawulok, M., M.E. Celebi and B. Smolka (Eds.). Springer, Cham, Switzerland, ISBN: 978-3-319-25956-7, pp: 63-100.

Mazumdar, D., M.K. Nayak and A.K. Talukdar, 2015. Adaptive Hand Segmentation and Tracking for Application in Continuous Hand Gesture Recognition. In: Recent Trends in Intelligent and Emerging Systems, Sarma, K.K., M.P. Sarma and M. Sarma (Eds.). Springer, New Delhi, India, ISBN: 978-81-322-2406-8, pp: 115-124.

Michal, K., N. Jakub and K. Jolanta, 2014. Skin Detection and Segmentation in Color Images. In: Advances in Low-Level Color Image Processing, Celebi, M.E. and B. Smolka (Eds.). Springer, Berlin, Germany, ISBN: 978-94-007-7583-1, pp: 329-366.

Mohandes, M., S. Aliyu and M. Deriche, 2014. Arabic sign language recognition using the leap motion controller. Proceedings of the 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), June 1-4, 2014, IEEE, Istanbul, Turkey, pp: 960-965.

Mohandes, M., S. Aliyu and M. Deriche, 2015. Prototype Arabic sign language recognition using multi-sensor data fusion of two leap motion controllers. Proceedings of the 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD’15), March 16-19, 2015, IEEE, Mahdia, Tunisia, pp: 1-6.

Mohandes, M.A. and S. Buraiky, 2007. Automation of the Arabic sign language recognition using the powerglove. AIML. J., 7: 41-46.
Direct Link  |  

Mohandes, M.A., 2013. Recognition of two-handed Arabic signs using the CyberGlove. Arabian J. Sci. Eng., 38: 669-677.
CrossRef  |  Direct Link  |  

Molchanov, P., X. Yang, S. Gupta, K. Kim, S. Tyree and J. Kautz, 2016. Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’16), June 27-30, 2016, IEEE, Las Vegas, Nevada, USA., pp: 4207-4215.

Najera, L.O.R., M.L. Sanchez, J.G.G. Serna, R.P. Tapia and J.Y.A. Llanes, 2016. Recognition of mexican sign language through the leap motion controller. Proceedings of the International Conference on Scientific Computing and (CSC’16), July 25-28, 2016, Las Vegas, Nevada, USA., pp: 147-151.

Natarajan, P. and R. Nevatia, 2007. Coupled hidden semi markov models for activity recognition. Proceedings of the 2007 IEEE Workshop on Motion and Video Computing (WMVC’07), February 23-24, 2007, IEEE, Austin, Texas, USA., pp: 10-10.

Nayak, S., S. Sarkar and B. Loeding, 2009. Automated extraction of signs from continuous sign language sentences using iterated conditional modes. Proceedings of the 2009 IEEE International Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, IEEE, Miami, Florida, USA., pp: 2583-2590.

Ong, E.J., H. Cooper, N. Pugeault and R. Bowden, 2012. Sign language recognition using sequential pattern trees. Proceedings of the 2012 IEEE International Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, IEEE, Providence, Rhode Island, USA., pp: 2200-2207.

Ong, E.J., O. Koller, N. Pugeault and R. Bowden, 2014. Sign spotting using hierarchical sequential patterns with temporal intervals. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, IEEE, Columbus, Ohio, USA., pp: 1923-1930.

Pashaloudi, V.N. and K.G. Margaritis, 2002. Hidden markov model for sign language recognition: A review. Proceedings of the 2nd Hellenic Conference on Artificial Intelligence (SETN’02), April 11-12, 2002, Thessaloniki, Greece, pp: 343-354.

Paudyal, P., A. Banerjee and S.K. Gupta, 2016. Sceptre: A pervasive, non-invasive and programmable gesture recognition technology. Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI’16), March 7-10, 2016, ACM, Sonoma, California, USA., pp: 282-293.

Paul Ekman Group LLC., 2017. Ekman paul micro expressions. Paul Ekman Group, LLC., Oakland, California, USA.

Pigou, L., V.D.A. Oord, S. Dieleman, M. Van Herreweghe and J. Dambre, 2018. Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video. Int. J. Comput. Vision, 126: 430-439.
CrossRef  |  Direct Link  |  

Pitsikalis, V., S. Theodorakis and P. Maragos, 2010. Data-driven sub-units and modeling structure for continuous sign language recognition with multiple cues. Proceedings of the LREC International Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies (LREC’10), May 17-23, 2010, Valletta, Malta, pp: 196-203.

Rao, G.A. and P.V.V. Kishore, 2018. Selfie video based continuous Indian sign language recognition system. Ain Shams Eng. J., 9: 1929-1939.
CrossRef  |  Direct Link  |  

Rautaray, S.S. and A. Agrawal, 2015. Vision based hand gesture recognition for human computer interaction: A survey. Artif. Intell. Rev., 43: 1-54.
CrossRef  |  Direct Link  |  

Raziq, N. and S. Latif, 2016. Pakistan sign language recognition and translation system using leap motion device. Proceedings of the International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC’16), November 5-7, 2016, Springer, Berlin, Germany, pp: 895-902.

Sandbach, G., S. Zafeiriou, M. Pantic and L. Yin, 2012. Static and dynamic 3D facial expression recognition: A comprehensive survey. Image Vision Comput., 30: 683-697.
CrossRef  |  Direct Link  |  

Sarkar, S., B. Loeding and A.S. Parashar, 2010. Fusion of Manual and Non-Manual Information in American Sign Language Recognition. In: Handbook of Pattern Recognition and Computer Vision, Chen, C.H. (Ed.). World Scientific, Singapore, pp: 477-495.

Sarkar, S., B. Loeding, R. Yang, S. Nayak and A. Parashar, 2011. Segmentation-robust representations, matching and modeling for sign language. Proceedings of the CVPR 2011 International Workshops, June 20-25, 2011, IEEE, Springs, Colorado, USA., pp: 13-19.

Shaik, K.B., P. Ganesan, V. Kalist, B.S. Sathish and J.M.M. Jenitha, 2015. Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Proc. Comput. Sci., 57: 41-48.
CrossRef  |  Direct Link  |  

Shohieb, S.M., H.K. Elminir and A.M. Riad, 2015. Signsworld atlas: A benchmark Arabic sign language database. J. King Saud Univ. Comput. Inf. Sci., 27: 68-76.
CrossRef  |  Direct Link  |  

Shukor, A.Z., M.F. Miskon, M.H. Jamaluddin, F. bin Ali, M.F. Asyraf and M.B. bin Bahar, 2015. A new data glove approach for Malaysian sign language detection. Procedia Comput. Sci., 76: 60-67.
CrossRef  |  Direct Link  |  

Simao, M.A., P. Neto and O. Gibaru, 2016. Unsupervised gesture segmentation by motion detection of a real-time data stream. IEEE. Trans. Ind. Inf., 13: 473-481.
CrossRef  |  Direct Link  |  

Simos, M. and N. Nikolaidis, 2016. Greek sign language alphabet recognition using the leap motion device. Proceedings of the 9th Hellenic Conference on Artificial Intelligence (SETN'16), May 18-20, 2016, ACM, Thessaloniki, Greece, pp: 1-4.

Sreejith, M., S. Rakesh, S. Gupta, S. Biswas and P.P. Das, 2015. Real-time hands-free immersive image navigation system using Microsoft Kinect 2.0 and Leap Motion Controller. Proceedings of the 2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG’15), December 16-19, 2015, IEEE, Patna, India, pp: 1-4.

Starner, T. and A. Pentland, 1997. Real-Time American Sign Language Recognition from Video Using Hidden Markov Models. In: Motion-Based Recognition, Shah M. and R. Jain (Eds.). Springer, Dordrecht, Netherlands, ISBN: 978-90-481-4870-7, pp: 227.

Stokoe Jr, W.C., 2005. Sign language structure: An outline of the visual communication systems of the American deaf. J. Deaf Stud. Deaf Educ., 10: 3-37.
CrossRef  |  PubMed  |  Direct Link  |  

Stokoe, W.C., D.C. Casterline and C.G. Croneberg, 1976. A Dictionary of American Sign Language on Linguistic Principles. 2nd Edn., Linstok Press, USA., ISBN: 9780932130013, Pages: 346.

Sun, C., T. Zhang and C. Xu, 2015. Latent support vector machine modeling for sign language recognition with Kinect. ACM. Trans. Intell. Syst. Technol. (TIST), Vol. 6, No. 2. 10.1145/2629481

Sun, C., T. Zhang, B.K. Bao and C. Xu, 2013. Latent support vector machine for sign language recognition with Kinect. Proceedings of the 2013 IEEE International Conference on Image Processing, September 15-18, 2013, IEEE, Melbourne, Australia.,-pp: 4190.

Sutarman, M.B.A. Majid and J.B.M. Zain, 2013. Vision-based sign language recognition systems: A review. Proceedings of the 2013 International Conference on Computer Science and Information Technology (CSIT’13), June 16-19, 2013, Yogyakarta, Indonesia, pp: 195-200.

Theodorakis, S., V. Pitsikalis and P. Maragos, 2014. Dynamic-static unsupervised sequentiality, statistical subunits and lexicon for sign language recognition. Image Vision Comput., 32: 533-549.
Direct Link  |  

Theodorakis, S., V. Pitsikalis, I. Rodomagoulakis and P. Maragos, 2012. Recognitionwith raw canonical phonetic movement and handshape subunits on videos of continuous sign language. Proceedings of the 2012 19th IEEE International Conference on Image Processing, September 30- October 3, 2012, IEEE, Orlando, Florida, USA., pp: 1413-1416.

Tolba, M.F., A. Samir and M. Aboul-Ela, 2013. Arabic sign language continuous sentences recognition using PCNN and graph matching. Neural Comput. Appl., 23: 999-1010.
CrossRef  |  Direct Link  |  

Tran, D., L. Bourdev, R. Fergus, L. Torresani and M. Paluri, 2015. Learning spatiotemporal features with 3D convolutional networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV’15), December 7-13, 2015, IEEE, Santiago, Chile, pp: 4489-4497.

Tripathi, K. and N.B.G. Nandi, 2015. Continuous Indian sign language gesture recognition and sentence formation. Procedia Comput. Sci., 54: 523-531.
Direct Link  |  

Tu, Y.H., J. Du, L. Sun, F. Ma, H.K. Wang, J.D. Chen and C.H. Lee, 2019. An iterative mask estimation approach to deep learning based multi-channel speech recognition. Speech Commun., 106: 31-43.
Direct Link  |  

Tubaiz, N., T. Shanableh and K. Assaleh, 2015. Glove-based continuous Arabic sign language recognition in user-dependent mode. IEEE. Trans. Hum. Mach. Syst., 45: 526-533.
CrossRef  |  Direct Link  |  

Uluer, P., N. Akalın and H. Kose, 2015. A new robotic platform for sign language tutoring. Int. J. Soc. Rob., 7: 571-585.
CrossRef  |  Direct Link  |  

Villani, N.A. and K. Wright, 2007. SMILE: An immersive learning game for deaf and hearing children. Proceedings of the International Conference on ACM SIGGRAPH 2007 Educators Program, August 05-09, 2007, ACM, New York, USA., ISBN:978-1-4503-1830-3, pp: 1-17.

Viola, P. and M. Jones, 2001. Rapid object detection using a boosted cascade of simple features. Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1, December 8-14, 2001, Kauai, HI., USA., pp: 511-518.

Vogler, C. and D. Metaxas, 1997. Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics Computational Cybernetics and Simulation Vol. 1, October 12-15, 1997, IEEE, Orlando, Florida, USA., pp: 156-161.

Vogler, C. and D. Metaxas, 1999. Parallel hidden Markov models for American sign language recognition. Proceedings of the 7th IEEE International Conference on Computer Vision Vol. 1, September 20-27, 1999, IEEE, Kerkyra, Greece, pp: 116-122.

WHO., 2018. Center media deafness and hearing loss. World Health Organization, Geneva, Switzerland.

Wan, J., Y. Zhao, S. Zhou, I. Guyon, S. Escalera and S.Z. Li, 2016. Chalearn looking at people RGB-D isolated and continuous datasets for gesture recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 26-July 1, 2016, IEEE, Las Vegas, Nevada, USA., pp: 56-64.

Wang, H., P. Wang, Z. Song and W. Li, 2017. Large-scale multimodal gesture segmentation and recognition based on convolutional neural networks. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), October 22-29, 2017, IEEE, Venice, Italy, pp: 3138-3146.

Wang, N., X. Gao, D. Tao, H. Yang and X. Li, 2018. Facial feature point detection: A comprehensive survey. Neurocomputing, 275: 50-65.
Direct Link  |  

Wang, R.Y. and J. Popovic, 2009. Real-time hand-tracking with a color glove. ACM. Trans. Graphics, 28: 1-63.
CrossRef  |  Direct Link  |  

Wilbur, R. and A.C. Kak, 2006. Purdue RVL-SLLL American sign language database. Report, TR-06-12, Purdue University, West Lafayette, Indiana. https://engineering.purdue.edu/RVL/Database/ASL/asl-database-front.htm.bk.htm

Yan, J., Z. Lei, L. Wen and S.Z. Li, 2014. The fastest deformable part model for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, IEEE, Columbus, Ohio, USA., pp: 2497-2504.

Yang, H.D. and S.W. Lee, 2010. Robust sign language recognition with hierarchical conditional random fields. Proceedings of the 2010 20th International Conference on Pattern Recognition, August 23-26, 2010, IEEE, Istanbul, Turkey, pp: 2202-2205.

Yang, H.D. and S.W. Lee, 2011. Combination of manual and non-manual features for sign language recognition based on conditional random field and active appearance model. Proceedings of the 2011 International Conference on Machine Learning and Cybernetics (ICMLC’11) Vol. 4, July 10-13, 2011, IEEE, Guilin, China, pp: 1726-1731.

Yang, H.D. and S.W. Lee, 2013. Robust sign language recognition by combining manual and non-manual features based on conditional random field and support vector machine. Pattern Recognit. Lett., 34: 2051-2056.
Direct Link  |  

Yang, H.D., 2015. Sign language recognition with the kinect sensor based on conditional random fields. Sens., 15: 135-147.
CrossRef  |  PubMed  |  Direct Link  |  

Yang, H.D., S. Sclaroff and S.W. Lee, 2008. Sign language spotting with a threshold model based on conditional random fields. IEEE. Trans. Pattern Anal. Mach. Intell., 31: 1264-1277.
CrossRef  |  Direct Link  |  

Yang, R. and S. Sarkar, 2006. Detecting coarticulation in sign language using conditional random fields. Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), August 20-24, 2006, IEEE, Hong Kong, China, pp: 108-112.

Yang, R., S. Sarkar and B. Loeding, 2007. Enhanced level building algorithm for the movement epenthesis problem in sign language recognition. Proceedings of the 2007 IEEE International Conference on Computer Vision and Pattern Recognition, June 17-22, 2007, IEEE, Minneapolis, Minnesota, pp: 1-8.

Yang, R., S. Sarkar and B. Loeding, 2009. Handling movement epenthesis and hand segmentation ambiguities in continuous sign language recognition using nested dynamic programming. IEEE. Trans. Pattern Anal. Mach. Intell., 32: 462-477.
CrossRef  |  Direct Link  |  

Yang, W., J. Tao and Z. Ye, 2016. Continuous sign language recognition using level building based on fast hidden Markov model. Pattern Recognit. Lett., 78: 28-35.
CrossRef  |  Direct Link  |  

Yu, S.H., C.L. Huang, S.C. Hsu, H.W. Lin and H.W. Wang, 2011. Vision-based continuous sign language recognition using product HMM. Proceedings of the 1st Asian Conference on Pattern Recognition, November 28, 2011, IEEE, Beijing, China, pp: 510-514.

Zadghorban, M. and M. Nahvi, 2018. An algorithm on sign words extraction and recognition of continuous Persian sign language based on motion and shape features of hands. Pattern Anal. Appl., 21: 323-335.
CrossRef  |  Direct Link  |  

Zafeiriou, S., C. Zhang and Z. Zhang, 2015. A survey on face detection in the wild: past, present and future. Comput. Vision Image Understanding, 138: 1-24.
CrossRef  |  Direct Link  |  

Zafrulla, Z., H. Brashear, P. Presti, H. Hamilton and T. Starner, 2011. CopyCat: An American sign language game for deaf children. Proceedings of the Face and Gesture 2011, March 21-25, 2011, IEEE, Santa Barbara, California, pp: 647-647.

Zhang, J., W. Zhou and H. Li, 2014. A threshold-based hmm-dtw approach for continuous sign language recognition. Proceedings of the International Conference on Internet Multimedia Computing and Service (ICIMCS'14), July 10-12, 2014, ACM, Xiamen, China, pp: 237-240.

Zhang, J., W. Zhou and H. Li, 2015. A new system for Chinese sign language recognition. Proceedings of the 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), July 12-15, 2015, IEEE, Chengdu, China, pp: 534-538.