DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915934186242048 |
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| author | Gavas, Ekta Banerjee, Sudipta Hegde, Chinmay Memon, Nasir |
| author_facet | Gavas, Ekta Banerjee, Sudipta Hegde, Chinmay Memon, Nasir |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_13292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation Gavas, Ekta Banerjee, Sudipta Hegde, Chinmay Memon, Nasir Computer Vision and Pattern Recognition 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. |
| title | DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.13292 |