Feature Extraction Using DWT with Application to Offline Signature Identification

Abstract

Handwritten signature is most widely accepted biometrics for person identification. This paper proposes a novel algorithm for offline handwritten signature recognition. Target of this research is to present signature recognition based on coded wavelet coefficient. It works at global level for extraction of discriminate signature features using wavelet transform. Before extracting the features, preprocessing of a scanned handwritten signature image is necessary to isolate the signature part and to remove any unwanted background present. Wavelet transform has been used to extract features from preprocessed signature images. Wavelet coefficients are extracted from detail part of handwritten signature and further wavelet coefficients are coded. Wavelet coefficient coding results in image compression. This causes reduced feature vector size. Hamming distance has been used to find out distance between test signature pattern and training signature pattern. Experiments are carried on signature database for 56 users each of 24 genuine and 9 skilled forgery signatures. One more experiment is carried out on gathered database. Recognition success rate for genuine signatures is 95 %. FAR of proposed algorithm is about 0.22.