In a soft biometric-based model, multiple soft biometric characteristics are fused with one or more primary biometric traits in a multimodal environment. In this study, the authors have reviewed a Bayesian decision theory-based fusion technique and considerably improved its performance by first identifying some of its limitations and subsequently modifying it accordingly. Specifically speaking, they have utilised the notion of Gaussian functions and a novel dynamic soft biometric weight assignment (DSWA) scheme for achieving these objectives. They have tested the modified framework on real-life data, which resulted in improved performances over the basic fusion model. They have also attempted to address here some security and privacy concerns associated with such frameworks. Although the soft biometric characteristics possess much lower uniqueness in comparison to a primary trait, they can be exploited by an active adversary to mine sensitive information about any individual. As such, the authors have proposed a secure fusion technique which performs one-way transformations of the soft biometric characteristics. They have also tested this secure design on real-life data and found the results satisfying.