Metric for Evaluating Performance of Reference-Free Demorphing Methods
This work provides a standardized evaluation framework for demorphing techniques, which is crucial for improving security in biometric systems, though it is incremental as it focuses on metric development rather than a new demorphing method.
The paper addresses the lack of consensus in evaluating reference-free face demorphing methods by proposing a new metric called biometrically cross-weighted IQA, which overcomes shortcomings in existing metrics and is validated through experiments on three methods and six datasets using two face matchers.
A facial morph is an image created by combining two (or more) face images pertaining to two (or more) distinct identities. Reference-free face demorphing inverts the process and tries to recover the face images constituting a facial morph without using any other information. However, there is no consensus on the evaluation metrics to be used to evaluate and compare such demorphing techniques. In this paper, we first analyze the shortcomings of the demorphing metrics currently used in the literature. We then propose a new metric called biometrically cross-weighted IQA that overcomes these issues and extensively benchmark current methods on the proposed metric to show its efficacy. Experiments on three existing demorphing methods and six datasets on two commonly used face matchers validate the efficacy of our proposed metric.