TY - GEN N2 - 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. ID - discovery10115642 UR - https://doi.org/10.1007/978-3-642-40303-3_14 EP - 135 T3 - Lecture Notes in Computer Science book series Y1 - 2013/// SN - 1611-3349 TI - Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data AV - public PB - Springer, Berlin, Heidelberg A1 - Bautista, MÁ A1 - Hernández-Vela, A A1 - Ponce, V A1 - Perez-Sala, X A1 - Baró, X A1 - Pujol, O A1 - Angulo, C A1 - Escalera, S KW - Depth maps KW - Gesture Recognition KW - Dynamic Time Warping KW - Statistical Pattern Recognition N1 - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions. SP - 126 ER -