Signature recognition

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

Off-Line Signature Recognition and Verification Using Neural Network

Abstract: In this paper, we present an off-line signature recognition and verification system using global and grid features of the signatures An artificial neural network based on back propagation algorithm is used for recognition and verification Performance measures like the learning rate FAR and FRR are analyzed The system was tested with 400 test signature samples, which include genuine and forgery signatures of twenty individuals With this system, a false rejection ratio of less than 01 and a false acceptance ratio of less than 02 are achieved

System and method of biometric authentication of electronic signatures using iris patterns

Abstract: The use of iris recognition to authenticate the signatory to an electronic document is provided. A system and method are described which permit capture of handwritten graphic signatures and true identity through an iris-based biometric and association of these data with electronic documents. The system and method include capture and storage of a powerful biometric identifier based on the iris of the eye which uniquely identifies and binds the signatory to the signature and the document. A biometric record is produced which contains information about the document, such as, for example, the conditions under which it was signed, the reason for signing as understood by the signatory, the biometric template of the signatory, and a graphic representation of the signature. Stored with the document, this biometric record allows later detection of fraud associated with the signature, including forgery, replacement of the signature, alteration of the document, or alteration of the signature object itself.

A system for fingerprint minutiae classification and recognition

Abstract: This paper shows a study about biometrics characteristics for recognition/classification and presents how it is used for individual recognition. The approach uses the automated fingerprint recognition based on minutia, which are extracted directly from the finger prints and the methodology used to its recognition is the artificial neural networks (ANN) based system. The neocognitron model was the ANN chosen. Inasmuch as neocognitron was originally implemented for handwritten characters recognition, it is possible to verify its usefulness for another kind of pattern recognition. Finally it is presented the results for this system and the conclusions according to the number of samples and recognition rate.

Signature Recognition & Verification System Using Back Propagation Neural Network

Abstract: The fact that the signature is widely used as a means of personal identification tool for humans require that the need for an automatic verification system. Verifwication can be performed either Offline or Online based on the application. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this paper, off-line signature recognition & verification using back propagation neural network is proposed, where the signature is captured and presented to the user in an image format. 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. Before extracting the features, preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. The system is initially trained using a database of 56 persons signatures obtained from those 56 individuals whose signatures have to be authenticated by the system. For each subject a mean signature is obtained integrating the above features derived from a set of his/her genuine sample signatures .This signature recognition& verification system is designed using MATLAB. This work has been tested and found suitable for its purpose.

A New Multimodal Biometric Recognition System Integrating Iris, Face and Voice

Abstract: The recognition accuracy of unimodal biometric systems has to contend with a variety of problems such as background noise, noisy data, non-universality, spoof attacks, intra-class variations, inter-class similarities or distinctiveness, interoperability issues. This paper describes a new multimodal biometric system that integrates multiple traits of an individual for recognition, which is able to alleviate the problems faced by unimodal biometric system while improving recognition performance. We have developed a multimodal biometric system by combining iris, face and voice at match score level using simple sum rule. The match scores are normalized by min-max normalization. The identity established by this system is much more reliable and precise than the individual biometric systems. Experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system. The effectiveness of proposed system regarding FAR (False Accept Rate) and GAR (Genuine Accept Rate) is demonstrated with the help of MUBI (Multimodal Biometrics Integration) software.