OFF - LINE SIGNATURE RECOG NITION USING MODIFIE D NEURAL NETWORKS APPROACH

Iometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capability to reliably distinguish between an authorized person and an imposter. Signature verification systems can be categorize d as offline (static) and online (dynamic). This paper presents neural network based recognition of offline signatures system that is trained with low - resolution scanned signature images. The signature of a person is an important biometric attribute of a h uman being which can be used to authenticate human identity. 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 signatu re recognition. There are various approaches to signature recognition with a lot of scope of research. In this paper, off - line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on parameters extracted from the signature using various image processing techniques. The Off - line Signature Recognition and Verification is implemented using Matlab. This work has been tested and found sui table for its purpose. For the implementation of this proposed work we use the Matlab software