Comprehensive Survey of Performance of Techniques for Hand Gesture Recognition System for Sign Languages
Keywords:
HGRS, SVM, Histogram, BOF, DSL, ISL, ASL, SIFT, K-nearest neighbor, PCA.Abstract
Hand Gesture Recognition System (HGRS) used for human-computer interaction. HGRS is having phases which are applied on captured image. The image is passes through phases like Hand Detection, Region of Extraction, Feature Extraction, Feature Matching, Pattern Recognition. There are many algorithms and techniques used for the phases in HGRS. There are various techniques used to improve the performance of HGRS in feature extraction and feature matching. This comprehensive study of techniques used for feature extraction and feature matching will elaborate performance of these techniques for various purpose in HGRS. For the various phases in HGRS, different algorithms can be used for feature extraction and feature matching like K-nearest neighbor, Support Vector Machine, BOF and SIFT, etc. The performances of the various existing algorithms is discussed.
References
- Bhumika Gupta, Pushkar Shukla, Ankush Mittal."K-Nearest Correlated Neighbor Classification for Indian Sign Language Gesture Recognition using Feature Fusion" 2016 International Conference on Computer Communication and Informatics (ICCCI -2016), Jan. 07 – 09, 2016, Coimbatore, INDIA.
- Jayshree. R. Pansare, Maya Ingle."Comprehensive Performance Study of Existing Techniques in Hand Gesture Recognition System for Sign Languages",(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 7 (3) , 2016, 1343-134.
- Jayshree R. Pansare, Kirti S. Rampurkar, Pritam L. Mahamane, Reshma J. Baravkar, Sneha V. Lanjewar."Real-Time Static Devnagri Sign Language Translation using Histogram", International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 4, April 2013.
- V.Adithya., P. R. Vinod, and U. Gopalakrishnan."Artificial neural network based method for Indian sign language recognition", In IEEE Conference on Information & Communication Technologies (ICT), pp. 1080-1085. IEEE, 2013.
- G.Khurana, G. Joshi & J.Kaur,"Static hand gestures recognition system using shape based features", in Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1-4,March 2014.
- S.C. Agarwal, A.S. Jalal, C. Bhatnagar ,"Recognition of Indian Sign Language using feature fusion",4th International Conference on Intelligent Human Computer Interaction, pp.1-5,2012.
- S.C. Agarwal, A.S. Jalal, C. Bhatnagar ,"Recognition of Indian Sign Language using feature fusion",4th International Conference on Intelligent Human Computer Interaction, pp.1-5,2012.
- Baoyun Zhang, Ruwei Yun*."Robust Gesture Recognition Based on Distance Distribution Feature and Skin-Color Segmentation", 978 DOI : 10.1109/ICALIP.2010.5685201
- Dipak Kumar Ghosh, Samit Ari. "A Static Hand Gesture Recognition Algorithm Using K-Mean Based Radial Basis Function Neural Network", 978-1-4577-0031-6/11/ 2011 IEEE ICICS 2011.
- Ravikiran J, Kavi Mahesh, SuhasMahishi, Dheeraj R, Sudheender S, Nitin V Pujari."Finger Detection for Sign Language Recognition", Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol IIMECS 2009, March 18 - 20, 2009.
- Weijer, Joost, and Fahad Shahbaz Khan."Fusing color and shape for bag-of-words based object recognition" In Computational Color Imaging, Springer Berlin Heidelberg, 2013, pp. 25-34.
- S.Quansen, S.ZENG, P.A. Heng, and D.S Xia."The theory of canonical correlation analysis and its application to feature fusion". In Chinese journal of computers-chinese edition- 28, no. 9 (2005): 1524.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.