Total views : 403
A Hybrid Gesture Recognition Method for American Sign Language
Gesture based communication is a method of correspondence between the ordinary and hard of hearing people in which the vision based procedure is utilized. This paper proposes a novel methodology of hand gesture recognition system for American Sign Language (ASL), which will perceive communication via gestures signals in an ongoing situation. A hybrid based descriptor, which joins the benefits of LBP (Local binary pattern), SP (super pixels) and SURF (Speeded Up Robust Features) strategies, is utilized as a consolidated list of capabilities to accomplish a improved identification rate beside among a little moment in time computational difficulty. In additional increase the detection speed and create the appreciation framework strong to view-point varieties, the idea of derived features from the accessible list of capabilities is presented. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are utilized for hybrid arrangement of single marked letter. Comparative investigation of these strategies with other well known methods demonstrates that the constant proficiency and robustness are better. The performances parameters will be used in this method are accuracy, sensitivity, precision, FNR and FDR.
American Sign Language, FNR, FDR, Hand Gesture Recognition, KNN, LBP, Recognition Rate, SP, SURF, SVM
- Wang Y, Yang R. Real-time hand posture recognition based on hand dominant line using kinect. 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), San Jose, CA. 2013. p. 1–4.
- Singh A, Arora S, Shukla P, Mittal A. Indian Sign Language gesture classification as single or double handed gestures. 2015 Third International Conference on Image Information Processing (ICIIP), Waknaghat. 2015. p. 378–81.
- Singh N, Baranwal N, Nandi GC. Implementation and evaluation of DWT and MFCC based ISL gesture recognition. 2014 9th International Conference on Industrial and Information Systems (ICIIS), Gwalior. 2014. p. 1–7.
- Rekha J, Bhattacharya J, Majumder S. Shape, texture and local movement hand gesture features for Indian Sign Language recognition. 3rd International Conference on Trendz in Information Sciences and Computing (TISC2011), Chennai. 2011. p. 30–5.
- Nagadeepa C, Balaji N, Padmaja V. An Efficient Framework for 2-Dimensional Gesture Based Telugu Character Recognition. 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram. 2016.p. 446–50.
- Pan TY, Lo LY, Yeh CW, Li JW, Liu HT, Hu MC. Real-Time Sign Language Recognition in Complex Background Scene Based on a Hierarchical Clustering Classification Method. 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), Taipei. 2016. p. 64–7.
- Savur C, Sahin F. Real-Time American Sign Language Recognition System Using Surface EMG Signal. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL. 2015.p. 497–502.
- Ghosh DK, Ari S. Static Hand Gesture Recognition using Mixture of Features and SVM Classifier. 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT), Gwalior. 2015. p.1094–9.
- Huan L, Bo R. Human gesture recognition based on image sequences. Control Conference (CCC), 2015 34th Chinese, Hangzhou. 2015. p. 8388–92.
- Kuroki K, Zhou Y, Cheng Z, Lu Z, Zhou Y, Jing L. A remote conversation support system for deaf-mute persons based on bimanual gestures recognition using finger-worn devices. 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), St. Louis, MO. 2015. p. 574–8.
- Ni Z, Fu S, Tang B, He H, Huang X. Experimental studies on indoor sign recognition and classification. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Orlando, FL. 2014; 489–94.
- Viswanathan DM, Idicula SM. Recognition of hand gestures of English alphabets using HOG method. 2014 International Conference on Data Science and Engineering (ICDSE), Kochi. 2014. p. 219–23.
- Plawiak P, Sosnicki T, Niedzwiecki M, Tabor Z, Rzecki K. Hand Body Language Gesture Recognition Based on Signals From Specialized Glove and Machine Learning Algorithms. IEEE Transactions on Industrial Informatics. 2016 Jun; 12(3):1104–13.
- Huang J, Zhou W, Li H, Li W. Sign language recognition using real-sense. 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), Chengdu. 2015. p. 166–70.
- Aryanie D, Heryadi Y. American sign language-based finger-spelling recognition using k-Nearest Neighbors classifier. 2015 3rd International Conference on Information and Communication Technology (ICoICT ), Nusa Dua. 2015. p. 533–6.
- Wahyono, Jo KH. A comparative study of classification methods for traffic signs recognition. 2014 IEEE International Conference on Industrial Technology (ICIT), Busan. 2014. p. 614–9.
- Sinith MS, Kamal SG, Surendran NS, JPS. Sign Gesture Recongnition using Support Vector Machine. 2012 International Conference on Advances in Computing and Communications (ICACC), Cochin, Kerala. 2012. p. 122–5.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.