Online signature verification using singletemplate matching with timeseries averaging and gradient boosting Abstract   In keeping with recent developments in artificial intelligence in the era of big data, there is a demand for online signature verification systems that operate at high speeds, provide a high level of security, and allow high tolerances while achieving sufficient performance. In response to these needs, the present study proposes a novel, singletemplate strategy using a mean template set and weighted multiple dynamic time warping (DTW) distances for a functionbased approach to online signature verification. Specifically, to obtain an effective mean template for each feature while reflecting intrauser variability between all the reference samples, we adopt a novel timeseries averaging method based on Euclidean barycenterbased DTW barycenter averaging. Then, by using the mean template set, we calculate multiple DTW distances from multivariate time series based on dependent and independent warping. Finally, to boost the discriminative power, we apply a weighting scheme using a gradient boosting model to efficiently combine the multiple DTW distances. Experimental results using the common SVC2004 Task1Task2 and MCYT100 signature datasets confirm that the proposed method is effective for online signature verification.

Deep Dynamic Time Warping: EndtoEnd Local Representation Learning for Online Signature Verification Siamese networks have been shown to be successful in learning deep representations for multivariate time series verification. However, most related studies optimize a global distance objective and suffer from a low discriminative power due to the loss of temporal information. To address this issue, we propose an endtoend, neural networkbased framework for learning local representations of time series, and demonstrate its effectiveness for online signature verification. This framework optimizes a Siamese network with a local embedding loss, and learns a feature space that preserves the temporal locationwise distances between time series. To achieve invariance to nonlinear temporal distortion, we propose building a dynamic time warping block on top of the Siamese network, which will greatly improve the accuracy for local correspondences across intrapersonal variability. Validation with respect to online signature verification demonstrates the advantage of our framework over existing techniques that use either handcrafted or learned feature representations.