Nearest neighbour classification of Indian sign language gestures using kinect camera
People with speech disabilities communicate in sign language and therefore have trouble in mingling with the able-bodied. There is a need for an interpretation system which could act as a bridge between them and those who do not know their sign language. A functional unobtrusive Indian sign language...
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| Published in | Sadhana (Bangalore) Vol. 41; no. 2; pp. 161 - 182 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New Delhi
Springer India
01.02.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0256-2499 0973-7677 |
| DOI | 10.1007/s12046-015-0405-3 |
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| Abstract | People with speech disabilities communicate in sign language and therefore have trouble in mingling with the able-bodied. There is a need for an interpretation system which could act as a bridge between them and those who do not know their sign language. A functional unobtrusive Indian sign language recognition system was implemented and tested on real world data. A vocabulary of 140 symbols was collected using 18 subjects, totalling 5041 images. The vocabulary consisted mostly of two-handed signs which were drawn from a wide repertoire of words of technical and daily-use origins. The system was implemented using Microsoft Kinect which enables surrounding light conditions and object colour to have negligible effect on the efficiency of the system. The system proposes a method for a novel, low-cost and easy-to-use application, for Indian Sign Language recognition, using the Microsoft Kinect camera. In the fingerspelling category of our dataset, we achieved above 90% recognition rates for 13 signs and 100% recognition for 3 signs with overall 16 distinct alphabets (A, B, D, E, F, G, H, K, P, R, T, U, W, X, Y, Z) recognised with an average accuracy rate of 90.68%. |
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| AbstractList | People with speech disabilities communicate in sign language and therefore have trouble in mingling with the able-bodied. There is a need for an interpretation system which could act as a bridge between them and those who do not know their sign language. A functional unobtrusive Indian sign language recognition system was implemented and tested on real world data. A vocabulary of 140 symbols was collected using 18 subjects, totalling 5041 images. The vocabulary consisted mostly of two-handed signs which were drawn from a wide repertoire of words of technical and daily-use origins. The system was implemented using Microsoft Kinect which enables surrounding light conditions and object colour to have negligible effect on the efficiency of the system. The system proposes a method for a novel, low-cost and easy-to-use application, for Indian Sign Language recognition, using the Microsoft Kinect camera. In the fingerspelling category of our dataset, we achieved above 90% recognition rates for 13 signs and 100% recognition for 3 signs with overall 16 distinct alphabets (A, B, D, E, F, G, H, K, P, R, T, U, W, X, Y, Z) recognised with an average accuracy rate of 90.68%. |
| Author | ANSARI, ZAFAR AHMED HARIT, GAURAV |
| Author_xml | – sequence: 1 givenname: ZAFAR AHMED surname: ANSARI fullname: ANSARI, ZAFAR AHMED organization: Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur – sequence: 2 givenname: GAURAV surname: HARIT fullname: HARIT, GAURAV email: gharit@iitj.ac.in organization: Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur |
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| Cites_doi | 10.1109/ICMI.2002.1166990 10.1109/DICTAP.2012.6215407 10.1109/TPAMI.1986.4767749 10.1080/757582976 10.1007/11744023_32 10.1109/TPAMI.1986.4767748 10.1023/A:1008344623873 10.1007/978-1-4302-3868-3 10.1145/2398356.2398381 10.1007/978-1-4471-4640-7_7 10.4249/scholarpedia.10491 10.1007/978-3-642-25330-0_37 10.1016/S0146-664X(81)80009-3 10.4108/ICST.INTETAIN2008.2476 10.1023/B:VISI.0000029664.99615.94 10.1109/ICCVW.2011.6130290 10.1109/ROBOT.2009.5152473 10.1109/ICSIPA.2011.6144163 10.1109/AFGR.1996.557247 10.1109/ICRA.2011.5980567 10.1109/ICPR.2006.327 10.1007/978-3-642-96868-6_57 10.1109/WACV.2011.5711485 10.1109/CVPR.2005.38 10.1007/978-1-4757-6465-9 10.1109/34.735811 10.1109/TISC.2011.6169079 10.1016/B978-0-12-407701-0.00001-7 10.1109/AFGR.1998.671007 10.1109/CVPR.2001.990517 10.1023/A:1008045108935 10.1109/ICOM.2011.5937178 10.1109/CVPR.2007.383124 10.1145/2072298.2071946 10.1109/IROS.2010.5651280 10.14569/IJACSA.2013.040228 10.1109/CVPRW.2008.4563023 10.1109/34.868688 10.1109/TSMCC.2007.893280 10.1145/1315575.1315577 |
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| References_xml | – reference: Saengsri S, Niennattrakul V and Ratanamahatana C 2012 Tfrs: Thai finger-spelling sign language recognition system. In: Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), 2012, pages 457–462 – reference: Rekha J, Bhattacharya J and Majumder S 2011 Shape, texture and local movement hand gesture features for Indian Sign Language recognition. In: 3rd International Conference on Trendz in Information Sciences and Computing (TISC), 2011, pages 30–35 – reference: LindebergTScale invariant feature transformScholarpedia2012751049110.4249/scholarpedia.10491 – reference: Bundy A and Wallen L 1984 Difference of gaussians. In: Bundy A and Wallen L (eds) Catalogue of artificial intelligence tools, symbolic computation, page 30. Springer, Berlin Heidelberg – reference: BabaudJWitkinAPBaudinMDudaROUniqueness of the gaussian kernel for scale-space filteringIEEE Trans. Pattern Anal. Mach. Intell.198681263310.1109/TPAMI.1986.47677490574.93054 – reference: Van den Bergh M and Van Gool L 2011 Combining RGB and ToF cameras for real-time 3D hand gesture interaction. In: IEEE Workshop on Applications of Computer Vision (WACV), 2011, pages 66–72 – reference: Singha J and Das K 2013 Indian sign language recognition using eigen value weighted euclidean distance based classification technique. arXiv preprint arXiv:1303.0634 – reference: Bilal S, Akmeliawati R, El Salami M J and Shafie A A 2011 Vision-based hand posture detection and recognition for sign language–a study. In: 4th International Conference on Mechatronics (ICOM), 2011, pages 1–6 – reference: Kenn H, Megen F V and Sugar R 2007 A glove-based gesture interface for wearable computing applications. In: 4th International Forum on Applied Wearable Computing (IFAWC), 2007, pages 1–10 – reference: Viola P and Jones M 2001 Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001. vol. 1, pages I-511–I-518 – reference: Ren Z, Yuan J and Zhang Z 2011 Robust hand gesture recognition based on finger-earth mover’s distance with a commodity depth camera. In: Proceedings of the 19th ACM international conference on Multimedia, MM ’11, pages 1093–1096, New York, NY, USA – reference: StarnerTWeaverJPentlandAReal-time american sign language recognition using desk and wearable computer based video.IEEE Trans. Pattern Anal. Mach. Intell.199820121371137510.1109/34.735811 – reference: MitraSAcharyaTGesture recognition: A surveyIEEE Trans. Syst. Man Cybern. Part C: Appl. Rev.200737331132410.1109/TSMCC.2007.893280 – reference: Rusu R B and Cousins S 2011 3D is here: Point cloud library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), 2011, pages 1–4 – reference: Buades A, Coll B and Morel J M 2005 A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 2, pages 60–65 – reference: Kramer J, Parker M, Herrera D, Burrus N and Echtler F 2012 Hacking the kinect. Apress – reference: Lindeberg T 1993 Scale-space theory in computer vision. Springer – reference: LoweDGDistinctive image features from scale-invariant keypointsInt. J. Comp. Vis.20046029111010.1023/B:VISI.0000029664.99615.94 – reference: BulwerJPhilocopus, or the deaf and dumb man’s friend1648LondonHumphrey and Moseley – reference: BhatnagarSAdaptive newton-based multivariate smoothed functional algorithms for simulation optimizationACM Trans. Model. Comput. Simul.20071812:12:3510.1145/1315575.1315577 – reference: ShiJMalikJNormalized cuts and image segmentationIEEE Trans. Pattern Anal. Mach. Intell.200022888890510.1109/34.868688 – reference: LindebergTGeneralized axiomatic scale-space theoryAdv. Imaging Electron Phys.2013178110.1016/B978-0-12-407701-0.00001-7 – reference: Ansari Z A 2013b Gesture recognition for Indian sign language. Master’s thesis, Indian Institute of Technology Jodhpur, India – reference: ShottonJSharpTKipmanAFitzgibbonAFinocchioMBlakeACookMMooreRReal-time human pose recognition in parts from single depth imagesCommun. ACM201356111612410.1145/2398356.2398381 – reference: Tukey J W 1977 Exploratory data analysis. Reading, MA, 231 – reference: PrimeSense 2011 OpenNI platform 1.0 – reference: Muni B 1951 Natya Shastra. Calcutta: Asiatic Society of Bengal – reference: Geetha M and Manjusha U 2012 A vision based recognition of indian sign language alphabets and numerals using B-spline approximation. Int. J. Comp. Sci. Eng. (IJCSE) – reference: Bay H, Tuytelaars T and Van Gool L 2006 Surf: Speeded up robust features. 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| Title | Nearest neighbour classification of Indian sign language gestures using kinect camera |
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