Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.

Scaling and time warping in time series querying

Abstract: The last few years have seen an increasing understanding that Dynamic Time Warping (DTW), a technique that allows local flexibility in aligning time series, is superior to the ubiquitous Euclidean Distance for time series classification, clustering, and indexing. More recently, it has been shown that for some problems, Uniform Scaling (US), a technique that allows global scaling of time series, may just be as important for some problems. In this work, we note that for many real world problems, it is necessary to combine both DTW and US to achieve meaningful results. This is particularly true in domains where we must account for the natural variability of human action, including biometrics, query by humming, motion-capture/animation, and handwriting recognition. We introduce the first technique which can handle both DTW and US simultaneously, and demonstrate its utility and effectiveness on a wide range of problems in industry, medicine, and entertainment.

Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

Abstract: Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

A Probabilistic Framework for Matching Temporal Trajectories: CONDENSATION-Based Recognition of Gestures and Expressions

Abstract: The recognition of human gestures and facial expressions in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a framework for incremental recognition of human motion that extends the “Condensation” algorithm proposed by Isard and Blake (ECCV'96). Human motions are modeled as temporal trajectories of some estimated parameters over time. The Condensation algorithm uses random sampling techniques to incrementally match the trajectory models to the multi-variate input data. The recognition framework is demonstrated with two examples. The first example involves an augmented office whiteboard with which a user can make simple hand gestures to grab regions of the board, print them, save them, etc. The second example illustrates the recognition of human facial expressions using the estimated parameters of a learned model of mouth motion.