Local directional pattern based signature verification using weighted fractional distance classification

Authors:
Yaseen Moolla;Serestina Viriri;Jules R. Tapamo;Fulufhelo V. Nelwamondo
Affiliations:
University of KwaZulu-Natal, Durban, South Africa and Council for Scientific and Industrial Research, Pretoria, South Africa;University of KwaZulu-Natal, Durban, South Africa;University of KwaZulu-Natal, Durban, South Africa;Council for Scientific and Industrial Research, Pretoria, South Africa
Venue:
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
Year:
2012

Citing 7
Cited 0

When Is ''Nearest Neighbor'' Meaningful?

ICDT '99 Proceedings of the 7th International Conference on Database Theory
An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances

Pattern Recognition
Global Features for the Off-Line Signature Verification Problem

ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Off-line signature recognition using morphological pixel variance analysis

Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Improving performance of HMM-based off-line signature verification systems through a multi-hypothesis approach

International Journal on Document Analysis and Recognition
Gender Classification Using Local Directional Pattern (LDP)

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An introduction to biometric recognition

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Although handwritten signature verification has been extensively researched, it has not achieved optimum accuracy rate yet. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This paper presents an alternative efficient classification technique to supervised learning classification techniques. The signature features are extracted using the Local Directional Pattern algorithm, and classified using a combination of multiple distance-based techniques: weighted Euclidean distance, fractional distance and weighted fractional distance. This combination of multiple distance-based classification techniques achieved accuracy rate of 87.8%, which is comparable to a similar system that used Support Vector Machines, a supervised learning technique. Therefore, competitive levels of accuracy can be obtained using distance-based classification.