An Efficient Approach to Offline Signature Verification Based on Neural Network
Manju Rani Mathew · 2013
Biometrics plays an important role in personal identification and authentication. Signature is widely used as a means of personal verification systems. Signatures are accepted by governments and financial institutions as a legal means of verifying identity. This emphasizes the need for an automatic verification system. Unlike a password or a PIN, signature is unique to an individual and it is difficult to duplicate. The aim of this paper is to measure gray level features of an image when it is distorted by a complex background and train by using neural network classifier. The practical signature verification problems include problems due to the need of segmenting the signature from the image document. This problem is overcome in this paper by calculating the gray level distortion and segmenting the original signature from the complex backgrounds. Then the image is trained by a neural network by using feed forward back propagation algorithm.