This research will aim to address this gap. Research Objectives

The primary objective of this research is to develop a SIFT-based algorithm that will improve the efficacy of handwritten signature feature extractions and enable a more powerful and accurate tool for the assessment of signature validity, with lower rates of false rejection of valid signatures and false acceptance of forged (invalid) signatures. To this end, a comparison between measurements of Euclidean distance and Mahalanobis distance in signature features will be conducted, and the most efficacious, efficient, and accurate means of measurement will be identified. From this, it will be possible to construct a more effective and accurate algorithm as the parameters of that algorithm and the data utilized in its functioning will be more accurate and more effectively and comprehensively obtained. These objectives will serve the research community in addition to providing practical benefits in signature verification and validation, reducing costs associated with discovering and failing to discover forgeries...

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.
References

Mwangi, K. (2008). Offline handwritten signature verification using SIFT features. Makere University [dissertation].

Shkula, N. & Shandilya, M. (2010). Invariant features comparison in hidden markov model and SIFT for offline handwritten signature database. International Journal of Computer Applications 2(7): 31-4.