Off-line Signature Verification based on Angular Features

Prashanth, C. R.; Raja, K. B.

doi:10.7763/ijmo.2012.v2.166

Cited by 8 publications

(1 citation statement)

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“…C.R. Prashanth, et al [16]. It is found in this experiment by Authors that Transform domain pre-processing improves result.…”

Section: Literature Reviewmentioning

confidence: 49%

A Comparative Analysis on Offline Signature Recognition System Using Deep Convolutional Neural Network

Moud¹

2022

ECS Trans.

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The aim of signature recognition is to recognize name of author of the signature. It is a difficult work because scanned images are used for signature recognition. Identifying signatory is behavioral biometrics and has lot of potential of research in this area. There is a scope of accuracy improvement because of high variations of signature of same user. This paper investigates how can best feature extracted for signature recognition. In this research, pre-trained CNNs were used to fetch features and then fetched features were entered into machine learning algorithm, such as Random Forest, KNN, NN, and Decision Tree. This research is a comparative study on performance of model for particular datasets.

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“…C.R. Prashanth, et al [16]. It is found in this experiment by Authors that Transform domain pre-processing improves result.…”

Section: Literature Reviewmentioning

confidence: 49%

A Comparative Analysis on Offline Signature Recognition System Using Deep Convolutional Neural Network

Moud¹

2022

ECS Trans.

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Offline signature verification using support vectore machine

Robert

Thilagavathi

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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The analysis of dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Rotation invariant uniform local binary patterns and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses.This project is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features.The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. The results for local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

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An Offline Signature Recognition and Verification System Based on Neural Network

Khan¹,

Dhole²

2014

IJRET

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Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.

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Off-line Signature Verification based on Angular Features

Cited by 8 publications

References 0 publications

A Comparative Analysis on Offline Signature Recognition System Using Deep Convolutional Neural Network

A Comparative Analysis on Offline Signature Recognition System Using Deep Convolutional Neural Network

Offline signature verification using support vectore machine

An Offline Signature Recognition and Verification System Based on Neural Network

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