Decision Tree Based Single Person Gesture Recognition

  • Sriparna SahaEmail author
  • Shreyasi Datta
  • Amit Konar
Part of the Studies in Computational Intelligence book series (SCI, volume 837)


This chapter is aimed at gesture recognition using the Kinect sensor. The Kinect sensor generates the human skeleton with twenty different 3-dimensional coordinates corresponding to twenty body joints. The present work requires eleven out of these twenty joints: six joints about the right and the left hands and five upper body joints. A unique set of twenty three features has been extracted to distinguish between gestures corresponding to five basic human emotional states, namely, ‘Anger’, ‘Fear’, ‘Happiness’, ‘Sadness’ and ‘Relaxation’. The features are based on the distances, accelerations, and angle between the different joints. The goal of the proposed system is to classify an emotion as positive or negative and determine its intensity level from the corresponding gestures. A high overall recognition rate of 86.8% is obtained from the proposed system using a decision tree based classifier.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentMaulana Abul Kalam Azad University of TechnologyKolkataIndia
  2. 2.Electronics and Tele-communication Engineering DepartmentJadavpur UniversityKolkataIndia

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