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Bharatanatyam Hand Mudra Classification Using SVM Classifier with HOG Feature Extraction

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Innovations in Computer Science and Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 103))

Abstract

Communication is the ultimate of man’s search for conveying his ideas, emotions, and concepts. Dance is one of the media of communication through which dancers share notion of feelings, with the spectators through gestures, i.e., mudra. Gesture recognition propagates a concept without verbal speech or listening, and in dance recognition, the notion is transferred through various dance poses and actions. This activity in a way really paves way to enhance Indian Sign Language. This study focuses to solve the mudra resemblance in Bharatanatyam through a new system developed with image processing and classification technique using histogram of oriented gradient (HOG) feature extraction techniques and support vector machine (SVM) classifier. SVM classifies the features of HOG into mudras as text labels. Popular feature vectors such as scale-invariant feature transform (SIFT), speed up robust feature (SURF), and local binary pattern (LBP) are hardened against HOG for accuracy and speediness, and this innovative proposed concept is useful for online dance learners.

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Acknowledgements

The authors wish to thank all the members of Kerala Kalamandalam Cheruthuruthi. We would like to thank Dr. T. K. Narayanan (Vice chancellor, Kerala Kalamandalam) for the permission for the data collection that made all this work possible. A special thanks also to Bharatanatyam Students from Kalmandalam for the contribution of the data.

This study was approved by the Amrita Vishwa Vidyapeetham ethics committee and all the procedures performed in studies involving human participants were in accordance with the ethical standards of research committee.

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Authors and Affiliations

  1. Department of Computer Science & IT, Amrita Vishwa Vidyapeetham University, Kochi, India

    K. S. Varsha & Maya L. Pai

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  1. K. S. Varsha
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  2. Maya L. Pai
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Editors and Affiliations

  1. Guru Nanak Institutions, Hyderabad, Telangana, India

    Harvinder Singh Saini

  2. Guru Nanak Institutions, Hyderabad, Telangana, India

    Rishi Sayal

  3. School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India

    Govardhan Aliseri

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Varsha, K.S., Pai, M.L. (2020). Bharatanatyam Hand Mudra Classification Using SVM Classifier with HOG Feature Extraction. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_22

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  • DOI: https://doi.org/10.1007/978-981-15-2043-3_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2042-6

  • Online ISBN: 978-981-15-2043-3

  • eBook Packages: EngineeringEngineering (R0)

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