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

Abstract

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.

A Hybrid Method of Feature Extraction for Signatures Verification Using CNN and HOG a Multi-Classification Approach

“…Subramaniam et al [37] used CNN to improve signature forgery detection; this study indicated that CNN is more accurate and faster in the detection of forged signatures. Also, Kumar et al [38] used CNN to enhance signature verification, this work mentioned that using CNN obtained leading performance with a 3.56 average error rate (AER) on GPDS synthetic, 4.15 on CEDAR, and 3.51 on MCYT-75 datasets. Jindal et al [39] used two machine learning algorithms support vector machine and decision tree to verify signature, this study indicated that both algorithms achieved good results comparing to the previous works.…”

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

“…In this research work, the author used wellknown MLA approaches to examine actual diagnostic medical data based on various risk factors to assess their effectiveness for diabetic probability. Seven MLA were used in this study such as RF, KNN, MLP, SVC, GBC, DT, and LR [24][25][26][27][28][29][30][31][32]. Various statistical criteria were used to compare the analytical results.…”

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

“…The biometric system acts as a auspicious and continuously developing technology utilized in the automatic systems for finding an individual effectively without remembering or carrying anything, such as passwords and Ids [6][7][8][9]. Numerous research works that the iris trait has higher advantages compared to other biometric systems relevant to the features such as fingerprint and face, this makes the iris to be generally adopted in several applications for high-reliability and precise biometric systems [9][10][11][12]. The biometric system can be generally classified into 2 types, namely unimodal, and multimodal biometric systems [13].…”