Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Offline Signature Verification using FMCN with GA based Optimization of Features

Abstract: In recent years, along with extraordinary diffusion of internet and growing need of personal identification in many applications, signature verification is considered with interest. This paper proposed an offline signature verification method based on Genetic Algorithm and Fuzzy Min Max Neural Network Classifier with Compensatory Neuron. The proposed method is basically consists of two steps. At first step optimizing the features using genetic algorithm, and at second step signature recognition is done using Fuzzy Min Max Neural Network Classifier with Compensatory Neurons. The sample of signatures is used to represent a particular person. The sample signature is first preprocessed, and then features of the processed signature are extracted by using Krawtchouk moment. After feature extraction, these features are optimized by using genetic algorithm and finally optimized features are given to the classification phase for recognition. With this proposed method, we get the 98% accuracy in recognition and less time is required for classification with optimized features as compared to time required for classification without optimizing feature.

Offline signature identification using high intensity variations and cross over points based feature extraction

Abstract: Signature has its own advantage in person identification. The facts that people usually do not putting text in it; rather they draw a pattern as their signature. Even today, numbers of transactions are increasing related to banking and businesses are being identified via signatures. The main difficulty lies in the variations of the geometrical representation of the signature which is closely related to the identity of human beings. Hence, development methods for genuine signature verification must be needed. When bundles of documents, e.g. bank cheques, have to be verified in a limited time, the manual verification of account holders’ signatures is often tedious work. So there is a need of Automatic Signature Verification and Identification systems. For that different logic should be considered to process such signatures. The present paper is done in the field of offline signature identify by extracting some special domain features that make a signature difficult to forge. In this paper existing signature verification systems have been thoroughly studied and a model is designed to develop an offline signature idenfication system. Here off-line signature idenfication system that depends on high intensity variation based features as well as cross over points based features. Main aim is to take various feature points of a given signature and compares them with the test signatures feature points by choosing appropriate classifiers.

A Simple Signature Recognition System

Abstract: The signature of a person is an important biometric characteristic of a human being which can be used to verify human identity. Signature verification is an important research area in the field of authentication of a person as well as documents in e-commerce and banking. Signatures are verified based on features extracted from the signature using Invariant Central Moment and Modified Zernike moment for its invariant feature extraction because the signatures are Hampered by the large amount of variation in size, translation and rotation and shearing parameter. This signature recognition system is designed using MATLAB. This work has been tested and found suitable for its purpose. Keywords: Biometrics, Hidden Markov models (HMM), Normalized area of signature, Off-line Signature Recognition, OCR I. Introduction Biometrics is technologies used for measuring and analysing a person's unique characteristics. There are two types of biometrics: behavioral and physical. Behavioral biometrics are generally used for verification while physical biometrics can be used for either identification or verification. Among the different forms of biometric recognition systems such as fingerprint, iris, DNA, face, voice, vein structure palm etc.,In our society, traditional and accepted means for a person to identify and authenticate himself either to another human being or to a computer system is based on one or more of these three (3) general principles:  What the person knows  What he possesses  What he is The signature recognition & verification system shown in Fig. 1 is broadly divided into three subparts a) Preprocessing, b) Feature extraction, c) Recognition & Verification. The input signature is captured from the scanner or digital high pixel camera which provides the output image in term of BMP Color image. The preprocessing algorithm provides the required data suitable for the final processing. In the feature extraction phase the invariant central moment and Zernike moment are used to extract the feature for the classification purpose. In classification the Back propagation Neural Network is used to provide high accuracy and less computational complexity in training and testing phase of the system.