NDLI: A Compact Size Feature Set for the Off-Line Signature Verification Problem

Content Provider	IEEE Xplore Digital Library
Author	Vu Nguyen Blumenstein, M.
Copyright Year	2012
Abstract	With increasing computational power, researchers in the area of off-line signature verification have been able to investigate feature extraction techniques that produce large-dimensional feature vectors. However, a large feature vector is not necessarily associated with high performance. This paper investigates the performance of a small feature set consisting of 33 feature values. In the experiments using Support Vector Machines (SVMs), an average error rate (AER) of 16.80% was obtained together with a low false acceptance rate (FAR) for random forgeries of 0.19%. The significant reduction of the error rate was obtained when the proposed global features were employed, which demonstrates their astonishingly high discriminant power. These results suggest that the use of global features for the off-line signature verification problem is worth further investigation.
Starting Page	261
Ending Page	265
File Size	179166
Page Count	5
File Format	PDF
ISBN	9781467308687
e-ISBN	9780769546612
DOI	10.1109/DAS.2012.1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-27
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Energy feature Error analysis rotation invariant feature Off-line signature verification Feature extraction Forgery Vectors Trajectory Support Vector Machine Variance feature
Content Type	Text
Resource Type	Article

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