Invariant features comparison in hidden markov model and SIFT for offline handwritten signature database

Abstract

In Handwritten signatures analyzed for forgery have to undergo feature extraction process, due to varied samples in size rotation and intra-domain changes, invariance has to be achieved during feature extraction process; circular Hidden Markov Model with discrete radon transform approach of feature extraction provides invariance. On other hand Scale Invariant Feature Transform (SIFT) has inherent invariant feature extraction approach. This paper compares both approaches on common signature databases for False acceptance rate(FAR),False Rejection Rate(FRR) and Equal Error Rate(EER) Categories and Subject DescriptorsThe Paper deals in Digital Forensic category where circular Hidden Markov Model(HMM) and Scale invariant Feature Transform(SIFT) invariant features are compared for Offline Handwritten Signature verification General TermsHere we will be dealing with feature extraction of offline handwritten signature with Discrete Radon Transform (DRT) for circular HMM for forgery detection and in second approach scale invariant image features of offline signatures are extracted using SIFT and forgery detection is done.