Detection of Signature Based Forgeries Using Artificial Neural Network
G. Bharatha Sreeja1, T. M. Inbamalar2, S. Ciyamala Kushbu3, A. Lasipa4, A.S. Aileen Jocy5
1G. Bharatha Sreeja, RMK College of Engineering and Technology, Thiruvallur District, (Tamil Nadu), India.
2Dr. T. M. Inbamalar, RMK College of Engineering and Technology, Thiruvallur District, (Tamil Nadu), India.
3Ciyamala Kushbu, RMK College of Engineering and Technology, Thiruvallur District, (Tamil Nadu), India.
4A. Lasipa, PET Engineering College, (Tamil Nadu), India.
5A.S. Aileen Jocy, PET Engineering College, (Tamil Nadu), India.
Manuscript received on 26 November 2019 | Revised Manuscript received on 07 December 2019 | Manuscript Published on 14 December 2019 | PP: 400-403 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10791191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1079.1191S19
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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: Signature plays an important role in banking, financial, commercial etc. Signature may be unique for each person. In olden days, no techniques were used to find the forged signature and it becomes a tremendous strain for human brain. Sometimes the forged signature may also believe as an original one. But nowadays, there are so many methods to detect the forged signature. This paper explains about identifying the forged signature from original signature. The signatures are preprocessed then the features such area, centroid coordinate, eccentricity, kurtosis are extracted. Then it is classified using Artificial Neural Network effectively. The result is analyzed by changing the hidden nodes present in the Neural Network. The performance is evaluated using the parameters such as TPR, TNR, FPR and FNR.
Keywords: Artificial Neural Network, Preprocessing, Thinning and Dilation, Feature Extraction, TPR (True Positive Rate), TNR (True Negative Rate), FPR (False Positive Rate) and FNR (False Negative Rate).
Scope of the Article: Artificial Life and Societies