Improving Reliability of Manipuri Offline Signature Verification Using Writer Independent Paradigms Teressa Longjam, Dakshina R. Kisku, Phalguni Gupta Proceedings of SPIE the International Society for Optical Engineering, 2021 A person's signature is a significant biometric trait that can be used to identify the person, often required in financial transactions and insurance-related activities. It is well-known that during such transactions, handwritten forgery signatures made on behalf of a genuine user results stealing the money or other means of wealth which was safely kept in the bank or other institutions. Therefore, it is necessary to have a vital safeguard like an automated signature verification system against malicious offenders who threaten society. This paper reports a writer independent offline signature verification system that makes use of genuine and forged signatures written in Manipuri script. Further, a combination of handcrafted geometric features and the features extracted using Convolutional Neural Network (CNN) is used. Then, the combined features' feature space is made optimal using the Genetic Algorithm (GA). This system has achieved a very high-level performance using an ensemble of four pattern classifiers, Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes learning, and Decision Tree Learning. Ensembling of the classifiers is done using logical OR rule and Majority Voting. Experiments are conducted on an original database consisting of Manipuri signatures of 81 individuals. Experimental results are compelling, while the proposed offline signature verification system is compared with the existing system.
A supervised manipuri offline signature verification system with global and local features Teressa Longjam, Dakshina Ranjan Kisku 2017 7th International Symposium on Embedded Computing and System Design Ised 2017, 2017 Handwritten signature verification is one of the significant research area where writers are verified or identified by their signatures. Handwritten signatures can be found in many official documents in day to day applications where people are fond to use their own scripts for writing the signatures. Usually, human experts look for the pattern of a signature in order to verify an authenticated document. The same expertise or even better can be adopted into an algorithm and run on a computer system where handwritten signatures could be accurately verified with minimum effort and time. As it is a behavioural biometrics trait, therefore writing style would decide the complexity of signature patterns of individual writers. Manipuri or Meithei is one of the official languages of the Indian state Manipur where large number of native people speak Manipuri language. This paper proposes a supervised learning approach for verifying individuals using their handwritten offline signatures. To accomplish this task, a set of local and global features related to the structure of the signature is extracted from offline signature. Further, this set of features is used for matching and classification of signatures using Support Vector Machines. Evaluation is performed on an offline Manipuri signature database containing 630 genuine and 140 forged signatures contributed by 70 individuals. The experimental results are found to be encouraging and effective while a set of local and global features are used for capturing the overall pattern of a Manipuri signature.
RECENT SCHOLAR PUBLICATIONS
Writer independent handwritten signature verification on multi-scripted signatures using hybrid CNN-BiLSTM: A novel approach T Longjam, DR Kisku, P Gupta Expert Systems with Applications 214, 119111 , 2023 2023 Citations: 55
Multi-scripted writer independent off-line signature verification using convolutional neural network T Longjam, DR Kisku, P Gupta Multimedia Tools and Applications 82 (4), 5839-5856 , 2023 2023 Citations: 16
Improving reliability of manipuri offline signature verification using writer independent paradigms T Longjam, DR Kisku, P Gupta Thirteenth international conference on digital image processing (ICDIP 2021 … , 2021 2021 Citations: 3
A supervised manipuri offline signature verification system with global and local features T Longjam, DR Kisku 2017 7th International Symposium on Embedded Computing and System Design … , 2017 2017 Citations: 9
Comparative study of destination sequenced distance vector and Ad-hoc on-demand distance vector routing protocol of mobile Ad-hoc network T Longjam, N Bagoria International Journal of Scientific and Research Publications 3 (2), 1-7 , 2013 2013 Citations: 15
MOST CITED SCHOLAR PUBLICATIONS
Writer independent handwritten signature verification on multi-scripted signatures using hybrid CNN-BiLSTM: A novel approach T Longjam, DR Kisku, P Gupta Expert Systems with Applications 214, 119111 , 2023 2023 Citations: 55
Multi-scripted writer independent off-line signature verification using convolutional neural network T Longjam, DR Kisku, P Gupta Multimedia Tools and Applications 82 (4), 5839-5856 , 2023 2023 Citations: 16
Comparative study of destination sequenced distance vector and Ad-hoc on-demand distance vector routing protocol of mobile Ad-hoc network T Longjam, N Bagoria International Journal of Scientific and Research Publications 3 (2), 1-7 , 2013 2013 Citations: 15
A supervised manipuri offline signature verification system with global and local features T Longjam, DR Kisku 2017 7th International Symposium on Embedded Computing and System Design … , 2017 2017 Citations: 9
Improving reliability of manipuri offline signature verification using writer independent paradigms T Longjam, DR Kisku, P Gupta Thirteenth international conference on digital image processing (ICDIP 2021 … , 2021 2021 Citations: 3
