Online Signature Verification Using Normalized Dynamic Feature with Artificial Neural Network Classification
نویسندگان
چکیده
INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY ONLINE SIGNATURE VERIFICATION USING NORMALIZED DYNAMIC FEATURE WITH ARTIFICIAL NEURAL NETWORK CLASSIFICATION Manish Trikha , Maitreyee Dutta * M.E Scholar, Department of Electronics and Communication Engineering, National Institute of Technical Teacher Training and Research(NITTTR), Chandigarh, India. Professor & Head, Department of Electronics and Communication Engineering, National Institute of Technical Teacher Training and Research, Chandigarh, India.
منابع مشابه
Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classification
Signatures continue to be an important biometric for authenticating the identity of human beings. This paper presents an effective method to perform off-line signature verification using unique structural features extracted from the signature's contour. A novel combination of the Modified Direction Feature (MDF) and additional distinguishing features such as the centroid, surface area, length a...
متن کاملOffline Signature verification scheme using DTI and NN classifiers
Signature detection have a major issue in digital verification of signature without using online help. Handwritten signature is one of the most vastly accepted personal attributes for identity verification. The main area of research on signature verification is in the field of pattern recognition and image processing. Earlier different method of feature detection and classifications has been us...
متن کاملSignature Recognition and Verification using Hybrid Features and Clustered Artificial Neural Network(ANN)s
Signature represents an individual characteristic of a person which can be used for his / her validation. For such application proper modeling is essential. Here we propose an offline signature recognition and verification scheme which is based on extraction of several features including one hybrid set from the input signature and compare them with the already trained forms. Feature points are ...
متن کاملClassification Approaches in Off-Line Handwritten Signature Verification
The aim of off-line signature verification is to decide, whether a signature originates from a given signer based on the scanned image of the signature and a few images of the original signatures of the signer. Although the verification process can be thought to as a monolith component, it is recommended to divide it into loosely coupled phases (like preprocessing, feature extraction, feature m...
متن کاملA new signature verification technique based on a two-stage neural network classifier
This paper presents a new technique for off-line signature recognition and verification. The proposed system is based on global, grid and texture features. For each one of these feature sets a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage, the classifier combines the decision results of the neural networks and the Euc...
متن کامل