DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation

Ekta Gavas, Sudipta Banerjee, Chinmay Hegde, Nasir Memon

Keywords: Age estimation, Face recognition, Diffusion, Makeup Removal

Abstract: 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.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 13