A Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification

Abstract

Dynamic Time Warping (DTW) is a tried and tested online signature verification technique that still finds relevance in modern studies. However, DTW operates in a writer-dependent manner and its algorithm outputs unbounded distance values. The introduction of bounded outputs offers the prospect of cross pollination with other regression models which provide normalized outputs. Writer-dependent methods are heavily influenced by the richness of the available reference signature sets. Although writer-independent methods also use reference signatures, they have the ability to learn general characteristics of genuine and forged signatures. This ability particularly gives them an edge at detecting skilled forgeries. Noting that DTW, on the other hand, has a strength at random signature verification, this study proposes a model which combines DTW and Deep Neural Networks (DNNs). When trained on a class balanced training set from the BiosecurID dataset, using a best vs 1 reference signature selection scheme, the proposed hybrid model outperforms previous methods, achieving Equal Error Rates of 5.17 and 2.64 for skilled and random signature cases, respectively.