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Publication Type : Book Chapter
Publisher : Springer
Source : Lecture Notes in Networks and Systems, 2020, 103, pp. 175-183.
Url : https://link.springer.com/chapter/10.1007/978-981-15-2043-3_22
Campus : Kochi
School : School of Computing
Department : Computer Science
Year : 2020
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.
Cite this Research Publication : Varsha, K.S., Pai, M.L. "Bharatanatyam Hand Mudra Classification Using SVM Classifier with HOG Feature Extraction," Lecture Notes in Networks and Systems, 2020, 103, pp. 175-183.