Affiliation:
1. Maulana Abul Kalam Azad University of Technology, West Bengal, India
2. Jadavpur University, India
Abstract
With the advent of Kinect and other modern-day sensors, gesture recognition has emerged as one of the most promising research disciplines for developing innovative and efficient human-computer interaction platforms. In the present work, the authors aim to build an interactive system by combining the principles of pattern recognition along with the intelligent application of Kinect sensor. Involving Kinect sensor has served the purpose of collecting skeletal data, and after processing the same, the extracted relevant features have been fed to principal component analysis for dimensionality reduction phase. Finally, instead of using a single classifier for detection, in this chapter, an ensemble of k-nearest neighbor classifiers has been chosen since an ensemble algorithm is always likely to provide better results than a single classifier. To justify the efficacy of the designed framework it is implemented for interpretation of 20 distinct gestures, and in each case, it has generated better performances as compared to the other existing techniques.