Supervised Neural Network for Offline Forgery Detection of Handwritten Signature

2021 ◽  
Author(s):  
Saleem Summra ◽  
M. Ghani Usman ◽  
Aslam Muhammad ◽  
Martinez-Enriquez A. M
Keyword(s):  
Neural Network ◽  
Forgery Detection ◽  
Handwritten Signature

Valid Signature Detection and Verification for Security of Individual Person

2016 ◽  
Vol 1 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Varun Sharma ◽  
Narpat Singh
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Recent Work ◽  
Research Work ◽  
Early Research ◽  
Individual Person ◽  
Signature Detection ◽  
Online Signature ◽  
The Right ◽  
Handwritten Signature

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.

scholarly journals Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4520
Author(s):  
Luis Lopes Chambino ◽  
José Silvestre Silva ◽  
Alexandre Bernardino
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Facial Recognition ◽  
Spectral Band ◽  
Support Vector ◽  
Multispectral Images ◽  
Forgery Detection ◽  
K Nearest Neighbor ◽  
Deep Convolutional Neural Networks ◽  
Domain Specific

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.

An Automatic Off-Line Signature Verification and Forgery Detection System

2008 ◽  
pp. 63-89 ◽  
Author(s):  
Vamsi Krishna Madasu ◽  
Brian C. Lovell
Keyword(s):  
Membership Function ◽  
Detection System ◽  
Structural Parameters ◽  
Grid Method ◽  
Fuzzy Modeling ◽  
Signature Verification ◽  
Forgery Detection ◽  
Large Database ◽  
Factors Affecting ◽  
Handwritten Signature

This chapter presents an off-line signature verification and forgery detection system based on fuzzy modeling. The various handwritten signature characteristics and features are first studied and encapsulated to devise a robust verification system. The verification of genuine signatures and detection of forgeries is achieved via angle features extracted using a grid method. The derived features are fuzzified by an exponential membership function, which is modified to include two structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect other factors affecting the scripting of a signature. The efficacy of the proposed system is tested on a large database of signatures comprising more than 1,200 signature images obtained from 40 volunteers.

Sign in / Sign up

Export Citation Format

Share Document