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.

Complete signal modeling and score normalization for function-based dynamic signature verification

Abstract: In this contribution a function-based approach to on-line signature verification is presented. An initial set of 8 time sequences is used; then first and second time derivates of each function are computed over these, so 24 time sequences are simultaneously considered. A valuable function normalization is applied as a previous stage to a continuous-density HMM-based complete signal modeling scheme of these 24 functions, so no derived statistical features are employed, fully exploiting in this manner the HMM modeling capabilities of the inherent time structure of the dynamic process. In the verification stage, scores are considered not as absolute but rather as relative values with respect to a reference population, permitting the use of a best-reference score-normalization technique. Results using MCYT_Signature sub-corpus on 50 clients are presented, attaining an outstanding best figure of 0.35% EER for skilled forgeries, when signer-dependent thresholds are considered.

An Empirical Comparison of Dissimilarity Measures for Time Series Classification

Abstract: Distance and dissimilarity functions are of undoubted importance to Time Series Data Mining. There are literally hundreds of methods proposed in the literature that rely on a dissimilarity measure as the main manner to compare objects. One notable example is the 1-Nearest Neighbor classification algorithm. These methods frequently outperform more complex methods in tasks such as classification, clustering, prediction, and anomaly detection. All these methods leave open the distance or dissimilarity function, being Euclidean distance (ED) and Dynamic Time Warping (DTW) the two most used dissimilarity measures in the literature. This paper empirically compares 48 measures on 42 time series data sets. Our objective is to call the attention of the research community about other dissimilarity measures besides ED and DTW, some of them able to significantly outperform these measures in classification. Our results show that Complex Invariant Distance DTW (CIDDTW) significantly outperforms DTW and that CIDDTW, DTW, CID, Minkowski L-p (p-norm difference with data set-crafted "p" parameter), Lorentzian L-infinity, Manhattan L-1, Average L-1/L-infinity (arithmetic average), Dice distance, and Jaccard distance outperform ED, but only CIDDTW, DTW, and CID outperform ED with statistical significance.

Dynamic Time Warp Pattern Matching Using an Integrated Multiprocessing Array

Abstract: Dynamic time warping is a well-established technique for time alignment and comparison of speech and image patterns. This paper decribes the architecture, algorithms, and design of a CMOS integrated processing array used for computing the dynamic time warp algorithm. Emphasis is placed on speech recognition applications because of the real-time constraints imposed by isolated and continuous speech recognition.

FingerSound: Recognizing unistroke thumb gestures using a ring

Abstract: We introduce FingerSound, an input technology to recognize unistroke thumb gestures, which are easy to learn and can be performed through eyes-free interaction. The gestures are performed using a thumb-mounted ring comprising a contact microphone and a gyroscope sensor. A K-Nearest-Neighbor(KNN) model with a distance function of Dynamic Time Warping (DTW) is built to recognize up to 42 common unistroke gestures. A user study, where the real-time classification results were given, shows an accuracy of 92%-98% by a machine learning model built with only 3 training samples per gesture. Based on the user study results, we further discuss the opportunities, challenges and practical limitations of FingerSound when deploying it to real-world applications in the future.