Efficient Fusion based Directional and Textural features for Signature Verification
K N Pushpalatha1, A K Gautham2, Satish S B3, Sabyasachi Pattnaik4
1K N Pushpalatha, Research Scholar, Mewar University, Chittorgarh, Rajasthan, India.
2A K Gautham, Principal, S D College of Engineering, Muzaffarnagar, (U.P), India.
3Satish S B, Associate Professor, Department of ECE, Dayananda Sagar College of Engineering, Bangalore, India.
4Sabyasachi Pattnaik, Professor and HOD, Department of I&CT, Fakir Mohan University, Balasore, Odhsha, India.
Manuscript received on July 26, 2013. | Revised Manuscript received on August 10, 2013. | Manuscript published on August 30, 2013. | PP: 167-172 | Volume-2, Issue-6, August 2013. | Retrieval Number: F2015082613/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Biometric signature verification, nowadays an important technique to recognize human identity. The accuracy of signature verification has lot of scope for improvement. In this paper, we propose an offline signature verification using fusion of Directional and Textural features. The Image is preprocessed and divided into sub-bands by applying DWT. The Directional features- Gradient, Coherence, Orientation and Textural features- correlation, energy and homogeneity are computed from the sub-bands and concatenated to form feature vector. The Feed Forward ANN tool in MATLAB is used for classification and verification. The results of False Rejection Rate (FAR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are obtained for GPDS-960 database. A total of 204 images are used for training and testing. It is observed that the values of FRR, FAR and TSR are improved compared to the existing algorithms.
Keywords: ANN, Biometric, Coherence, DWT, Textural features.