%L discovery10115642 %K Depth maps, Gesture Recognition, Dynamic Time Warping, Statistical Pattern Recognition %I Springer, Berlin, Heidelberg %S Lecture Notes in Computer Science book series %O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. %X Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data. %B International Workshop on Depth Image Analysis and Applications WDIA 2012: Advances in Depth Image Analysis and Applications %V 7854 %A Mà Bautista %A A Hernández-Vela %A V Ponce %A X Perez-Sala %A X Baró %A O Pujol %A C Angulo %A S Escalera %T Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data %D 2013 %P 126-135