Multiple classifiers error rate optimization approaches of an automatic signature verification (ASV) system

• Main Author: Ahmad S.M.S.
• Other Authors: 24721182400
• Format: Conference paper
• Published: 2023
• Subjects: Automatic signature verification; Borda count; Equal error rate (EER); Multiple classifiers; Computer vision; Decision making; Face recognition; Learning systems; Object recognition; Pattern recognition systems; Rough set theory; Cascaded classifiers; Decision fusion methods; Decision levels; Equal error rate; Error rate; Error rate performance; Majority voting; Multi-stage; Multiple classifier approach; Optimization approach; Performance analysis; Performance improvements; Real applications; Static and dynamic; Sub-sets; Classifiers

Decision level management is a crucial aspect in an Automatic Signature Verification (ASV) system, due to its nature as the centre of decision making that decides on the validity or otherwise of an input signature sample. Here, investigations are carried out in order to improve the performance of an ASV system by applying multiple classifier approaches, where features of the system are grouped into two different subsets, namely static and dynamic sub-sets, hence having two different classifiers. In this work, three decision fusion methods, namely Majority Voting, Borda Count and cascaded multi-stage cascaded classifiers are analyzed for their effectiveness in improving the error rate performance of the ASV system. The performance analysis is based upon a database that reflects an actual user population in a real application environment, where as the system performance improvement is calculated with respect to the initial system Equal Error Rate (EER) where multiple classifiers approaches were not adopte.