Abstract
It is unclear whether Hidden Markov Models (HMMs) or Dynamic Time Warping (DTW) techniques are more appropriate for gesture recognition. In this paper, we compare both methods using different criteria, with the objective of determining the one with better performance. For this purpose we have created a set of recorded gestures. The dataset used includes many samples of ten different gestures, with their corresponding ground truth obtained with a kinect. The dataset is made public for benchmarking purposes. The results show that DTW gives higher performance than HMMs, and strongly support the use of DTW. © 2012 Springer-Verlag.
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CITATION STYLE
Carmona, J. M., & Climent, J. (2012). A performance evaluation of HMM and DTW for gesture recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7441 LNCS, pp. 236–243). https://doi.org/10.1007/978-3-642-33275-3_29
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