Improved Multi-Scale Local Difference Features for Off-Line Handwritten Signature Verification
Naouel Arab, Hassiba Nemmour, Youcef Chibani · 2020
In the present work, we propose the multi-scale Local Difference Features (LDF) for improving handwritten signature verification. This descriptor is similar to local binary patterns, since it highlights intensity differences between a pixel and its neighbors. Presently, we propose a multi-scale implementation of LDF in which, neighboring pixels are taken on several radiuses. This helps to adapt LDF to variations of handwritten strokes within signatures. The verification stage is carried out by using Support Vector Machines classifier. The performance assessment is elaborated on two benchmark datasets. Obtained error rates highlight the effectiveness of the proposed features, since they outperform several state of the art results.