Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 4406101 - 12
• Main Authors: Kumar, Rakesh, Saraswat, Mala, Ather, Danish, Mumtaz Bhutta, Muhammad Nasir, Basheer, Shakila, Thakur, R. N.
• Format: Journal Article
• Language: English
• Published: United States Hindawi 25-06-2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Affine transformations; Algorithms; Analysis; Artificial neural networks; Binary systems; Biometry - methods; Datasets; Decision making; Deformation; Forgery; Mental task performance; Neural networks; Neural Networks, Computer; Signatures; Teaching methods; Verification
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/4406101

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature’s scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.