2 research outputs found
Auto Signature Verification Using Line Projection Features Combined With Different Classifiers and Selection Methods
: Signature verification plays a role in the commercial, legal and financial fields. The signature continues to be one of the most preferred types of authentication for many documents such as checks, credit card transaction receipts, and other legal documents. In this study, we propose a system for validating handwritten bank check signatures to determine whether the signature is original or forged. The proposed system includes several steps including improving the signature image quality, noise reduction, feature extraction, and analysis. The extracted features depend on the signature line and projection features. To verify signatures, different classification methods are used. The system is then trained with a set of signatures to demonstrate the validity of the proposed signature verification system. The experimental results show that the best accuracy of 100% was obtained by combining several classification methods
Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification
This paper introduces a novel approach to leverage the knowledge of existing
expert models for training new Convolutional Neural Networks, on domains where
task-specific data are limited or unavailable. The presented scheme is applied
in offline handwritten signature verification (OffSV) which, akin to other
biometric applications, suffers from inherent data limitations due to
regulatory restrictions. The proposed Student-Teacher (S-T) configuration
utilizes feature-based knowledge distillation (FKD), combining graph-based
similarity for local activations with global similarity measures to supervise
student's training, using only handwritten text data. Remarkably, the models
trained using this technique exhibit comparable, if not superior, performance
to the teacher model across three popular signature datasets. More importantly,
these results are attained without employing any signatures during the feature
extraction training process. This study demonstrates the efficacy of leveraging
existing expert models to overcome data scarcity challenges in OffSV and
potentially other related domains