HMM-based gesture recognition system using kinect sensor for improvised human-computer interaction
Currently, gesture recognition from continuous video sequences is one of the most exciting research areas. This paper proposes a novel HMM-based gesture recognition scheme that can be implemented for developing an improved HCI system capable of providing enhanced performance. This framework explores the high potential of Microsoft's Kinect sensor in gesture recognition by utilizing it in the data acquisition phase. The primary novelty of the work lies in the choice of an active difference signature-based feature descriptor that contains time-warped information in a single sequence over the classically used geometric features. The discussed framework has been tested for 12 distinct gestures embodied by 60 different subjects and it is important to note that for all the gestures the proposed scheme has attained a fairly high recognition rate of nearly 90% which proves the worth of the present work in real time applications. Further, to check the efficacy of the newly formulated framework the performance of the same has been validated against the existing standard technologies.
Proceedings of the International Joint Conference on Neural Networks
Saha, S., Lahiri, R., Konar, A., Banerjee, B., & Nagar, A. (2017). HMM-based gesture recognition system using kinect sensor for improvised human-computer interaction. Proceedings of the International Joint Conference on Neural Networks, 2776-2783. https://doi.org/10.1109/IJCNN.2017.7966198