Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters

IEEE Transactions on Cybernetics
Stergios Poularakis, Ioannis Katsavounidis

Abstract

In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems.

References

Jan 15, 2005·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Li MaDexin Zhang
Jan 1, 1996·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·I KatsavounidisZ Zhang
May 16, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Hee-Deok YangSeong-Whan Lee
Jul 4, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jonathan AlonStan Sclaroff
Jun 11, 2011·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Vasiliki E KosmidouLeontios J Hadjileontiadis
Sep 22, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Mohamed-Bécha Kaâniche, François Brémond
Oct 11, 2012·IEEE Transactions on Cybernetics·Darya FrolovaSigal Berman
May 18, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yang YangMubarak Shah
Aug 6, 2013·IEEE Transactions on Cybernetics·Ling ShaoXuelong Li

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