Metric for Evaluating Performance of Reference-Free Demorphing Methods
원문 초록
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.
함께 살펴볼 논문
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MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
LDM-Morph: Latent diffusion model guided deformable image registration
NCA-Morph: Medical Image Registration with Neural Cellular Automata
LADIMO: Face Morph Generation through Biometric Template Inversion with Latent Diffusion
On the Feasibility of Creating Iris Periocular Morphed Images
Large Language Models for Multimodal Deformable Image Registration
토론
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