Bridging Gaps: An application of feature warping to online signature verification

The use of (online) signatures for the purpose of verifying a subject's identity is highly accepted within society and perceived as a noninvasive and nonthreatening biometric characteristic by most users. However, signature biometrics is typically characterized by a high intra-class variability, being influenced by several physical and emotional conditions, i.e. identity verification based on online signature biometrics represents an extremely challenging task. Online signature verification systems mainly utilize time-discrete signal processing techniques for biometric signature authorship verification. The vast majority of state-of-the-art approaches to online signature verification construct subject-specific probabilistic models during feature extraction, e.g. Gaussian Mixture Models (GMMs). Focusing on the construction of these models feature normalization turns out to be vital in order to achieve robustness against noise. In this work we propose the very first application of a feature normalization technique, referred to as Feature Warping (FW), which is well-established within the speaker recognition community, to a GMM-based online signature verification system. Experimental evaluations, which are carried out on the MCYT signature corpus, demonstrate that the presented adaptation of FW significantly improves the biometric performance of the underlying online signature verification system, achieving relative gains of approximately 47% in terms of equal error rates.