Paper Info
Title
PCVOS: Principal component variances based off-line signature verification
Abstract
Offline signature verification system is widely used as a behavioral biometric for identifying a person. This behavioral biometric trait is a challenge in designing the system that has to counter intrapersonal and interpersonal variations. In this paper, we propose a novel technique PCVOS: Principal Component Variances based Off-line Signature Verification on two critical parameters viz., the Pixel Density (PD) and the Centre of Gravity (CoG) distance. It consists of two parallel processes, namely Signature training which involves extraction of features from the samples of database and Test signature analysis which performs extraction of features from the test samples. The trained values from the database are compared with the features of the test signature using Principal Component Analysis (PCA). The PCVOS algorithm shows a notable improvement over the algorithms in [21], [22] and [23].
Year
DOI
Venue
2015
10.1109/ReTIS.2015.7232877
2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)
Keywords
Field
DocType
Biometrics,Off-line Signature Verification,Principal Components,Pixel Density,Centre of Gravity Distance
Data mining,Signature recognition,Pixel density,Off line,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Cog,Biometrics,Principal component analysis,Verification system
Conference
Citations 
PageRank 
References 
0
0.34
12
Authors
9
Name
Order
Citations
PageRank
J. S. Arunalatha100.34
C. R. Prashanth201.01
V. Tejaswi322.76
Shaila K473.66
K. B. Raja5349.60
Dinesh Anvekar601.35
K. R. Venugopal726748.80
S.S. Iyengar82923381.93
Lalit M. Patnaik924348.76