DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation
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Abstract (arXiv)
Accurate age verification can protect underage users from unauthorized access to online platforms and e-commerce sites that provide age-restricted services. However, accurate age estimation can be confounded by several factors, including facial makeup that can induce changes to alter perceived identity and age to fool both humans and machines. In this work, we propose DiffClean which erases makeup traces using a text-guided diffusion model to defend against makeup attacks. DiffClean improves age estimation (minor vs. adult accuracy by 5.8%) and face verification (TMR by 5.1% at FMR=0.01%) compared to images with makeup. Our method is robust across digitally simulated and real-world makeup styles, and outperforms multiple baselines in terms of biometric and perceptual quality. Our codes are available at https://github.com/Ektagavas/DiffClean.
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References · 32
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
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- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
- 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 429
- 2018MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devicescited by 182
- 2024Understanding Hallucinations in Diffusion Models through Mode Interpolationcited by 91
- 2019PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfercited by 66
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- 2019LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeupcited by 64
- 2020Lifespan Age Transformation Synthesiscited by 60
- 2024Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Modelcited by 45
- 2017Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approachcited by 43
- 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 43
- 2023MiVOLO: Multi-input Transformer for Age and Gender Estimationcited by 42
- 2021Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfercited by 35
- 2024DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protectioncited by 32
- 2022EleGANt: Exquisite and Locally Editable GAN for Makeup Transfercited by 28
- 2019RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for freecited by 24
- 2021FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wildcited by 20
- 2021PSGAN++: Robust Detail-Preserving Makeup Transfer and Removalcited by 17
- 2019Deep Differentiable Random Forests for Age Estimationcited by 16
- 2022BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfercited by 16
- 2024Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truthcited by 15
- 2024SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Modelscited by 10
- 2017Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verificationcited by 9
- 2019Soft-ranking Label Encoding for Robust Facial Age Estimationcited by 8
- 2025MAD: Makeup All-in-One with Cross-Domain Diffusion Modelcited by 5
- 2020Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimationcited by 3
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- 2026Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems
- 2026Face Age Verification Vulnerabilities Under Simple Appearance Manipulations
- 2023Identity-Preserving Aging of Face Images via Latent Diffusion Models
- 2025AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models
- 2024DiffFAS: Face Anti-Spoofing via Generative Diffusion Models
- 2025From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
- 2024DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
- 2026MakeupMirror: Improving Facial Attribute Preservation in Diffusion Models for Makeup Transfer