Metric for Evaluating Performance of Reference-Free Demorphing Methods

TL;DR

This paper introduces a new evaluation metric for reference-free face demorphing methods, addressing shortcomings of existing metrics and validating its effectiveness through extensive benchmarking on multiple datasets and methods.

Contribution

The paper proposes a novel biometrically cross-weighted IQA metric that improves evaluation of face demorphing techniques and provides a standardized benchmarking framework.

Findings

The new metric outperforms existing evaluation metrics.

Benchmark results show the effectiveness of the proposed metric.

Experiments validate the robustness across multiple datasets and face matchers.

Abstract

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.