Introducing Fuzziness on Snake Models for Off-Line Signature Verification: A Comparative Study

This paper presents an experimental comparison of different hybrid snake-based algorithms for automatic off-line signature verification. Snakes are usually applied to different image analysis tasks, especially to segmentation. Off-line signature verification aims to establish the degree of genuineness between one given test signature and one reference signature. Due to the intrapersonal variability in human signatures, a system with tolerance to imprecision seems be appropriate for this verification task. Our work aims to study how effective is introducing fuzziness to tackle this image analysis problem. We developed several hybrid snake algorithms adapted to the practical requirements of automatic signature verification. Fuzziness is introduced in the feature extraction stage and during the signature verification (or classification) stage. Our work compares four different hybrid snake-based approaches for this verification problem using the corresponding FAR and FRR biometric errors. Experimental results have shown that none of the tested approaches clearly outperforms the other ones.