Handwritten Signature Verification using Neural Network

Abstract

A number of biometric techniques have been proposed for personal identification in the past. Among the vision-based ones are face recognition, fingerprint recognition, iris scanning and retina scanning. Voice recognition or signature verification are the most widely known among the non-vision based ones. As signatures continue to play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. A signature by an authorized person is considered to be the "seal of approval" and remains the most preferred means of authentication. The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification. A verification stage includes applying the extracted features of test signature to a trained neural network which will classify it as a genuine or forged. KeywordsBiometrics, error back propagation algorithm, center of mass, neural network, and normalized area of signature.

Review on Offline Signature Recognition and Verification Techniques

“…Our recognition system exhibited 100% success rate by identifying correctly all the signatures that it was trained for [8]. We did not consider this a "high risk" case because recognition step is always followed by verification step and these kinds of false positives can be easily caught by the verification system.…”

“…An image also gets degraded because of the detrimental effects due to illumination and other objects in the environment. Median filter is extensively used for smoothing and restoring images corrupted by noise [8]. This is a nonlinear process useful principally in reducing impulsive noise [7].…”

“…In a median filter, a window slides over the image, and for each location of the window, the median concentration of the pixels within it decide the intensity of the pixel positioned in the middle of the window. As weigh against to the mean filter, median filter has striking properties for suppressing impulse noise while preserving edges; due to this feature we are recommended this filter in our proposed system [8].…”

Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM

“…The method of signature verification reviewed in this paper benefits the advantage of being highly accepted by potential customers. The use of the signature recognition has a long history which goes back to the appearance of writing itself [9]. Utilization of the signature as an authentication method has already become a tradition in the western civilization and is respected among the others.…”

An Appraisal of Off-line Signature Verification Techniques

“…Pansare et al [7] have presented a method for geometric feature extraction, preprocessing, training of neural network with extracted features and verification. The effectiveness of the signature verification system depends upon the set of chosen feature vectors for the Hidden Markov Model (HMM).…”