Signature Recognition and Verification using Hybrid Features and Clustered Artificial Neural Network(ANN)s
Signature represents an individual characteristic of a
person which can be used for his / her validation. For such application
proper modeling is essential. Here we propose an offline signature
recognition and verification scheme which is based on extraction of
several features including one hybrid set from the input signature
and compare them with the already trained forms. Feature points
are classified using statistical parameters like mean and variance.
The scanned signature is normalized in slant using a very simple
algorithm with an intention to make the system robust which is
found to be very helpful. The slant correction is further aided by the
use of an Artificial Neural Network (ANN). The suggested scheme
discriminates between originals and forged signatures from simple
and random forgeries. The primary objective is to reduce the two
crucial parameters-False Acceptance Rate (FAR) and False Rejection
Rate (FRR) with lesser training time with an intension to make the
system dynamic using a cluster of ANNs forming a multiple classifier
system.
[1] A. Prasad: An Offline Signature Verification System., Surathkal -574157, [email protected]..
[2] Y. Kato, D. Muramatsu and T. Matsumoto: "A Sequential Monte
Carlo Algorithm for Adaptation to Intersession Variability in On-line
Signature Verification", Department of Electrical Engineering and Bioscience,
Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555,
Japan,
[3] D. Jena, B. Majhi and S. K. Jena: "Improved Offline Signature
Verification Scheme Using Feature Point Extraction Method" , National
Institute of Technology Rourkela, Orissa, India, Journal of Computer
Science 4 (2): 111-116, 2008 ISSN 1549-3636 2008 Science Publications
[4] T. E. Emrezgndz and M. E. Karslgil: "OffLine Signature Verification
And Recognition By Support Vector Machine", Computer Engineering
Department, Yldz Technical University Yldz , Istanbul, Turkey,
[5] A. T. Wilson: "Offline Handwriting Recognition Using Articial Neural
Networks", University of Minnesota, Morris Morris.,
[6] C. B. Owen and F. Makedon: High Quality Alias Free Image Rotation,
Proceedings of 30th Asilomar Conference on Signals, Systems, and
Computers Pacific Grove, California, November 2-6, 1996.
[7] I. S. I. Abuhaiba: "Offine Signature Verication Using Graph Matching",
Department of Electrical and Computer Engineering, Islamic University
of Gaza,
[8] Mathworks: "Inc. Matlab Toolbox", http://www.mathworks.com,
[9] S. Haykin, Neural Networks A Comprehensive Foundation, Pearson
Education, 2nd edition, 2003.
[10] S. Kumar, Neural Networks A Classroom Approach, Tata McGraw
Hill, 8th Reprint, 2009.
[1] A. Prasad: An Offline Signature Verification System., Surathkal -574157, [email protected]..
[2] Y. Kato, D. Muramatsu and T. Matsumoto: "A Sequential Monte
Carlo Algorithm for Adaptation to Intersession Variability in On-line
Signature Verification", Department of Electrical Engineering and Bioscience,
Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555,
Japan,
[3] D. Jena, B. Majhi and S. K. Jena: "Improved Offline Signature
Verification Scheme Using Feature Point Extraction Method" , National
Institute of Technology Rourkela, Orissa, India, Journal of Computer
Science 4 (2): 111-116, 2008 ISSN 1549-3636 2008 Science Publications
[4] T. E. Emrezgndz and M. E. Karslgil: "OffLine Signature Verification
And Recognition By Support Vector Machine", Computer Engineering
Department, Yldz Technical University Yldz , Istanbul, Turkey,
[5] A. T. Wilson: "Offline Handwriting Recognition Using Articial Neural
Networks", University of Minnesota, Morris Morris.,
[6] C. B. Owen and F. Makedon: High Quality Alias Free Image Rotation,
Proceedings of 30th Asilomar Conference on Signals, Systems, and
Computers Pacific Grove, California, November 2-6, 1996.
[7] I. S. I. Abuhaiba: "Offine Signature Verication Using Graph Matching",
Department of Electrical and Computer Engineering, Islamic University
of Gaza,
[8] Mathworks: "Inc. Matlab Toolbox", http://www.mathworks.com,
[9] S. Haykin, Neural Networks A Comprehensive Foundation, Pearson
Education, 2nd edition, 2003.
[10] S. Kumar, Neural Networks A Classroom Approach, Tata McGraw
Hill, 8th Reprint, 2009.
@article{"International Journal of Electrical, Electronic and Communication Sciences:60127", author = "Manasjyoti Bhuyan and Kandarpa Kumar Sarma and Hirendra Das", title = "Signature Recognition and Verification using Hybrid Features and Clustered Artificial Neural Network(ANN)s", abstract = "Signature represents an individual characteristic of a
person which can be used for his / her validation. For such application
proper modeling is essential. Here we propose an offline signature
recognition and verification scheme which is based on extraction of
several features including one hybrid set from the input signature
and compare them with the already trained forms. Feature points
are classified using statistical parameters like mean and variance.
The scanned signature is normalized in slant using a very simple
algorithm with an intention to make the system robust which is
found to be very helpful. The slant correction is further aided by the
use of an Artificial Neural Network (ANN). The suggested scheme
discriminates between originals and forged signatures from simple
and random forgeries. The primary objective is to reduce the two
crucial parameters-False Acceptance Rate (FAR) and False Rejection
Rate (FRR) with lesser training time with an intension to make the
system dynamic using a cluster of ANNs forming a multiple classifier
system.", keywords = "offline, algorithm, FAR, FRR, ANN.", volume = "4", number = "8", pages = "1221-6", }