Author:
Sharifah M. Syed Ahmad
Affiliation:
Universiti Tenaga Tenaga Nasional (UNITEN), Malaysia
Keyword(s):
Multiple classifiers, Borda Count, Equal Error Rate (EER), Automatic Signature Verification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
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 sub- sets, namely static and dynamic subsets, 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 appro
aches were not adopted.
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