DocumentCode :
3585294
Title :
Combination of OC-LBP and Longest Run Features for Off-Line Signature Verification
Author :
Serdouk, Yasmine ; Nemmour, Hassiba ; Chibani, Youcef
Author_Institution :
Fac. of Electron. & Comput. Sci., Univ. of Sci. & Technol. Houari Boumediene, Algiers, Algeria
fYear :
2014
Firstpage :
84
Lastpage :
88
Abstract :
In this paper, we propose new data features to improve the off-line handwritten signature verification. The proposed features combine advantages of LBP and topological characteristics. Specifically, the Orthogonal Combination of LBP, which provides an LBP histogram with a reduced size, is combined with a topological descriptor that is called longest run features. The verification task is achieved by SVM classifiers and the performance assessment is conducted comparatively to the basic LBP descriptors. Results obtained on both GPDS 300 and CEDAR datasets show that the proposed features improve the verification accuracy while reducing the data size.
Keywords :
feature extraction; handwriting recognition; handwritten character recognition; image classification; support vector machines; topology; CEDAR dataset; GPDS 300 dataset; LBP descriptors; LBP histogram; OC-LBP; SVM classifiers; data features; data size reduction; longest-run features; off-line handwritten signature verification task; orthogonal combination; performance assessment; topological characteristics; topological descriptor; verification accuracy improvement; Accuracy; Forgery; Histograms; Support vector machines; Training; Local binary patterns; Longest run features; SVM; Signature verification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on
Type :
conf
DOI :
10.1109/SITIS.2014.36
Filename :
7081530
Link To Document :
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