Research

Dance Gesture Recognition: A Survey [PDF]

Gesture recognition means the identification of different expressions of human body parts to express the idea, thoughts and emotion. It is a multi-disciplinary research area. The application areas of gesture recognition have been spreading very rapidly in our real-life activities including dance gesture recognition. Dance gesture recognition means the recognition of meaningful expression from the different dance poses. Today, research on dance gesture recognition receives more and more attention throughout the world. The automated recognition of dance gestures has many applications. The motive behind this survey is to present a comprehensive survey on automated dance gesture recognition with emphasis on static hand gesture recognition. Instead of whole body movement, we consider human hands because human hands are the most flexible part of the body and can transfer the most meaning. A list of research issues and open challenges is also highlighted.

A two-level classification scheme for single-hand gestures of Sattriya dance

The single-hand gestures of Indian classical dance are termed as `Asamyukta Hastas' which is a combination of two Sanskrit words, asamyukta meaning `single' and hastas meaning `hand gestures'. This paper introduces a simple two-level classification method for asamyukta hastas of Sattriya dance which is an Indian classical dance form. In the first level, twenty nine classes of hastas are categorized into three groups based on their structural similarity. Then, in the next level hastas are individually recognized from the database within the group. Moreover, the proposed method extracts Medial Axis Transformation (MAT) from the captured images to identify the groups in the first level. One of the applications of the outcome of this research work can be in the e-learning and self learning of the dance hand gestures (mudras or hastas).

A Dataset of Single-Hand Gestures of Sattriya Dance

Datasets are important for validation of any method or technique. The effectiveness of a method or technique can be well judged using an unbiased, complete and correct dataset. This paper presents a novel dataset to support validation of any computer vision method for recognition of Sattriya dance hand gestures, a fifteenth-century major Indian classical dance of the state of Assam. The dataset fulfils all the major requirements and has been established using five well-known classifiers. The sample of dataset is made available at http://agnigarh.tezu.ernet.in/~dkb/resources.html.

An Empirical Analysis of Three Moments on Sattriya Dance Single-Hand Gestures Dataset

The single-hand gestures of Indian classical dance are termed as ‘Asamyukta Hastas,’ which is a combination of two Sanskrit words, asamyukta meaning ‘single’ and hastas meaning ‘hand gestures’. There are eight officially recognized classical dance forms in India. This paper focuses on the 29 single-hand gestures of Sattriya dance which is one of the Indian classical dance forms. It presents an analysis on recognition of single-hand gestures of Sattriya dance form images using different classifiers such as k-nearest neighbor (k-NN), naive Bayes, Bayesian network, decision tree, and Support Vector Machine (SVM). In this work, we have used Hu’s seven invariant moments, Zernike moments, and Legendre moments up to tenth order each. In this analysis, it indicates that Legendre moments show a better performance compared to other moments for all variation of dataset, and could achieve an accuracy of 96.03%.