Augmented Reality Face Filters: Identity, Technical Challenges, and Ethical Implications

Augmented Reality (AR) face filters have gained widespread adoption on social media, enabled by sophisticated image processing techniques that overlay digital effects on realtime video of faces. This paper presents a comprehensive study that integrates the technical foundations of AR face filters facial landmark detection, real-time face tracking, rendering, and occlusion handling—with an empirical survey of 410 users on issues of self-identity, privacy, and long-term psychological impacts. We review key engineering methods such as advanced facial feature tracking algorithms and occlusion-aware rendering, citing recent improvements that achieve high precision and speed. In parallel, we analyze survey results by gender and age group, examining Likert-scale responses about how AR filters affect users' self-perception and privacy attitudes. The results indicate nuanced user perspectives: while many enjoy the creativity of filters, a majority expressed concern about potential long-term effects on body image and authenticity. Notably, younger users reported greater awareness of privacy and psychological impacts compared to older users. A regression analysis suggests age is a significant predictor of perceived negative impacts (with users over 35 less concerned than teens, $\mathbf{p}<0.05)$, whereas gender and usage frequency showed no significant effect. By bridging technical insights with user-centered findings, this work highlights the need for responsible AR filter design that accounts for both cutting-edge performance and user well-being.