Improving Handwritten Signature-Based Identity Prediction through the Integration of Fuzzy Soft-Biometric Data

Abstract

Automated identification of individuals using biometric technologies is finding increasing application in diverse areas, yet designing practical systems can still present significant challenges. Choice of the modality to adopt, the classification/matching techniques best suited to the application, the most effective sensors to use, and so on, are all important considerations, and can help to ameliorate factors which might detract from optimal performance. Less well researched, however, is how to optimise performance by means of exploiting broader-based information often available in a specific task and, in particular, the exploitation of so-called "soft" biometric data is often overlooked. This paper proposes a novel approach to the integration of soft biometric data into an effective processing structure for an identification task by adopting a fuzzy representation of information which is inherently continuous, using subject age as a typical example. Our results show this to be a promising methodology with possible benefits in a number of potentially difficult practical scenarios.