Identity-Aware Facial Age Editing Using Latent Diffusion

Sudipta Banerjee;Govind Mittal;Ameya Joshi;Sai Pranaswi Mullangi;Chinmay Hegde;Nasir Memon
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Abstract

Aging in face images is a type of intra-class variation that has a stronger impact on the performance of biometric recognition systems than other modalities (such as iris scans and fingerprints). Improving the robustness of automated face recognition systems with respect to aging requires high quality longitudinal datasets that should contain images belonging to a large number of individuals collected across a long time span, ideally decades apart. Unfortunately, there is a dearth of such good operational quality longitudinal datasets. Synthesizing longitudinal data that meet these requirements can be achieved using modern generative models. However, these tools may produce unrealistic artifacts or compromise the biometric quality of the age-edited images. In this work, we simulate facial aging and de-aging by leveraging text-to-image diffusion models with the aid of few-shot fine-tuning and intuitive textual prompting. Our method is supervised using identity-preserving loss functions that ensure biometric utility preservation while imparting a high degree of visual realism. We ablate our method using different datasets, state-of-the art face matchers and age classification networks. Our empirical analysis validates the success of the proposed method compared to existing schemes. Our code is available at https://github.com/sudban3089/ID-Preserving-Facial-Aging.git
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利用潜在扩散进行身份识别面部年龄编辑
与其他模式(如虹膜扫描和指纹)相比,人脸图像中的衰老是一种对生物识别系统性能影响更大的类内变异。要提高自动人脸识别系统在老化方面的鲁棒性,需要高质量的纵向数据集,这些数据集应包含在较长的时间跨度(最好相隔几十年)内收集的属于大量个体的图像。遗憾的是,这种高质量的纵向数据集非常缺乏。使用现代生成模型可以合成符合这些要求的纵向数据。然而,这些工具可能会产生不切实际的假象,或影响年龄编辑图像的生物识别质量。在这项工作中,我们利用文本到图像的扩散模型,借助少量镜头微调和直观的文本提示来模拟面部老化和去老化。我们的方法使用身份保护损失函数进行监督,在保证生物识别效用的同时,还赋予了高度的视觉真实感。我们使用不同的数据集、最先进的人脸匹配器和年龄分类网络来消减我们的方法。我们的实证分析验证了与现有方案相比,我们提出的方法是成功的。我们的代码可在 https://github.com/sudban3089/ID-Preserving-Facial-Aging.git
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期刊最新文献
2024 Index IEEE Transactions on Biometrics, Behavior, and Identity Science Vol. 6 Table of Contents IEEE T-BIOM Editorial Board Changes IEEE Transactions on Biometrics, Behavior, and Identity Science Cutting-Edge Biometrics Research: Selected Best Papers From IJCB 2023
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