Journal
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Volume 7, Issue 3, Pages 966-977Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2012.2190281
Keywords
Application of support vector machine (SVM); biometrics; histogram SVM kernels; local binary patterns; local directional patterns; offline signature verification; texture features
Funding
- Spanish Government [MCINN TEC2009-14123-C04]
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Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP8,1riu2 plus LBP16,2riu2 and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white nondistorting background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.
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