提出新评估指标，更好衡量无参考人脸去融合效果
Metric for Evaluating Performance of Reference-Free Demorphing Methods
- 设计生物特征加权的图像质量评估新方法
- 在六大数据集上验证了现有方法的性能差异
- 适合研究人脸重建与评估的学者参考
人脸融合图是由两个（或更多）不同身份的人脸图像合成生成的。无参考人脸去融合旨在不依赖额外信息的情况下还原构成融合图的原始人脸图像。然而，目前学术界尚未就该任务的评估指标达成共识。本文首先分析现有评估方法的缺陷，随后提出一种名为生物特征交叉加权图像质量评估（biometrically cross-weighted IQA）的新指标，克服了原有方法的不足。我们在六个数据集和两种常用人脸匹配器上对三种现有去融合方法进行了广泛评测，结果验证了所提指标的有效性。
原文摘要 · Abstract (English)
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.
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