Abstract
Gestures are signals that contain important spatiotemporal information. Understanding gestures is a trivial task for humans, but for machines it is a challenging task involving thousands of computations per video frame. This paper investigates an efficient hand gesture recognition technique which is based on time projections of the hand location. For recognition, non-linear classifiers, namely Support Vector Machines and Artificial Neural Networks, are tested. The proposed method performs much faster than the conventional Markov Model based gesture recognition techniques while achieving comparable recognition results.
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Ahmad, O., Bona, B., Anjum, M.L., Khosa, I. (2014). Using Time Proportionate Intensity Images with Non-linear Classifiers for Hand Gesture Recognition. In: Mat Sakim, H., Mustaffa, M. (eds) The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications. Lecture Notes in Electrical Engineering, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-4585-42-2_40
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DOI: https://doi.org/10.1007/978-981-4585-42-2_40
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