Performance comparison of local directional pattern to local binary pattern in off-line signature verification system

There are several papers about pseudo dynamic methods used in signature authentication. Recently, the gray scale features local binary pattern(LBP) originate from texture analysis has been widely used in signature verification system with advantage of robustness to illumination change. The major problem of LBP is its sensitivity to noise, hence many solutions has been applied to solve this problem. In this paper, we further study the performance of LBP in terms of different blocks, then Local directional pattern is explored to obtain a stable and effective feature with the same blocks as LBP. The experiments done with GPDS960Graysignature database demonstrate the effectiveness of LBP and LDP, LBP performs a little better than LDP while LBP has higher dimensions than LDP while the classifier is deployed by Linear Support Vector Machines (SVMs).

[1]  Réjean Plamondon,et al.  Automatic Signature Verification: The State of the Art - 1989-1993 , 1994, Int. J. Pattern Recognit. Artif. Intell..

[2]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[3]  Oksam Chae,et al.  Local Directional Pattern (LDP) for face recognition , 2010, 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE).

[4]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[5]  Miguel A. Ferrer,et al.  Signature verification using local directional pattern (LDP) , 2010, 44th Annual 2010 IEEE International Carnahan Conference on Security Technology.

[6]  David Haussler,et al.  Proceedings of the fifth annual workshop on Computational learning theory , 1992, COLT 1992.

[7]  Sargur N. Srihari,et al.  Learning strategies and classification methods for off-line signature verification , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[8]  A. N. Rajagopalan,et al.  Off-line signature verification using DTW , 2007, Pattern Recognit. Lett..

[9]  Jesús Francisco Vargas-Bonilla,et al.  Robustness of Offline Signature Verification Based on Gray Level Features , 2012, IEEE Transactions on Information Forensics and Security.

[10]  Graham Leedham,et al.  Global Features for the Off-Line Signature Verification Problem , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[11]  Flávio Bortolozzi,et al.  An Off-Line Signature Verification System Using HMM and Graphometric Features , 2001 .

[12]  Juan Hu,et al.  Offline Signature Verification Using Real Adaboost Classifier Combination of Pseudo-dynamic Features , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[13]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[14]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Jesús Francisco Vargas-Bonilla,et al.  Off-line signature verification based on grey level information using texture features , 2011, Pattern Recognit..

[16]  Cheng Wang,et al.  A novel extended local-binary-pattern operator for texture analysis , 2008, Inf. Sci..

[17]  Giuseppe Pirlo,et al.  Automatic Signature Verification: The State of the Art , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Haris Baltzakis,et al.  A new signature verification technique based on a two-stage neural network classifier , 2001 .