On the separation of shape and temporal patterns in time series -Application to signature authentication-

TL;DR

This paper introduces a probabilistic alignment method to separate shape and temporal patterns in time series, demonstrating improved signature authentication performance over existing methods.

Contribution

It adapts a centroid estimation algorithm for shape-temporal separation in time series, applied to signature authentication benchmarks.

Findings

Empirically effective on synthetic data

Achieves slight improvement over state-of-the-art in signature tasks

Demonstrates practical benefit of shape-temporal separation

Abstract

In this article we address the problem of separation of shape and time components in time series. The concept ofshape that we tackle is termed temporally neutral to consider that it may possibly exist outside of any temporal specification, as it is the case for a geometric form. We propose to exploit and adapt a probabilistic temporal alignment algorithm, initially designed to estimate the centroid of a set of time series, to build some heuristicelements of solution to this separation problem. We show on some controlled synthetic data that this algorithm meets empirically our initial objectives. We finally evaluate it on real data, in the context of some on-line handwritten signature authentication benchmarks. On the three evaluated tasks, our approach based on the separation of signature shape and associated temporal patterns is positioned slightly above the current state of the art demonstrating the applicative benefit of this separating problem.