Abstract:
This paper presents the convolutional neural network for feature extraction and Support vector machine for the verification of offline signatures. The cropped signatures ...Show MoreMetadata
Abstract:
This paper presents the convolutional neural network for feature extraction and Support vector machine for the verification of offline signatures. The cropped signatures are used to train CNN for extracting features. The Extracted features are classified into two classes genuine or forgery using SVM. The the new signature is tested on GPDS signature data base using the trained SVM. The dabase contains signatures of 960 users and for each user there are 24 genuine signatures and 30 forgeries. The CNN network is trained with 300 users and signatures of 400 users are used for feature learning. These 400×20×25 signatures are used 90%to train and 10% to test SVM classifier.
Published in: 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Date of Conference: 08-10 November 2018
Date Added to IEEE Xplore: 15 August 2019
ISBN Information: