SIFT-based algorithms for handwritten signature verification and forgery detection

Biometric Signature

Biometric Handwritten Signature

The use of Scale Invariant Features Transform (SIFT) measurements for the verification of handwritten signatures is increasingly well-established in the literature, and has been demonstrated as an effective means of verification and of forgery detection through empirical research (Mwangi, 2008). Ongoing research has also shown that SIFT-based algorithms for measurement and decision-making in regards to signatures and potential forgeries is more effective than the Hidden Markov Model that has been utilized more frequently in practical applications as well as in research, and advances in the specific measurements used in SIFT algorithms and mechanisms continue to increase the efficacy of this methodology while raising new research questions and potentils at the same time (Shkula & Shandilya, 2010). Further comparison and potential combination of these methods and of certain of their individual processes and parts is necessary to determine best practices in handwritten signature measurement and the creation of variance by a host of biometric factors (Shkula & Shandilya, 2010). Though substantial amounts of research into handwriting and specifically signature analysis have already been achieved in previous research, ongoing investigations in these areas are called for both by the practical community and by researchers uncertain of recent findings and established knowledge (Mwangi, 2008; Shkula & Shandilya, 2010).

Research Problem

While SIFT algorithms have proven a reliable and effective means of engaging in handwritten signature verification, there is not a clear and empirically established best system of measurement for the creation of raw data necessary in running SIFT algorithms. That is, there are multiple measurements and means of measurement that can and have been utilized with varying degrees of success in the creation of SIFT algorithms and the establishing of effective verification means, however a comparison of these means of measurement and their impact on SIFT efficacy has yet to be conducted (Shkula & Shandilya, 2010). Advances in the use of SIFT algorithms and mechanisms for handwriting analysis have both addressed and exacerbated this problem by making new recommendations that are not entirely in keeping with previous findings, leaving the full scope of SIFT efficacy and the most beneficial and accurate measurements to be used in SIFT algorithms as unknowns. This research will aim to address this gap.

Significance

Though electronic and online signatures are becoming more common, offline handwritten signatures are still highly important in terms of establishing identity and forming contracts in a variety of real-world applications. This research will identify and promote best practices in signature validation, creating direct practical benefits in terms of security and contract validity through the application of knowledge obtained through this research. Algorithm and measurement assessments will also benefit the research community by identifying new areas for inquiry and new levels of certainty for established and/or recommended practices.