Privacy-Preserving Age Estimation for Content Rating

Linwei Ye, Binglin Li, Noman Mohammed, Yang Wang, Jie Liang

Abstract: Content rating (aka. maturity rating) rates the suitability of kinds of media (e.g., movies and video games) to its audience. It is essential to prevent a specific age group of people such as children from inappropriate information. However, in practice the administration of content rating system is usually suggestion-based declaration by media sources or key-based password which can easily fail if someone ignores the suggestions or somehow knows the keys. In this paper, we propose to estimate user's age in a privacy-preserving manner for automatic content rating. Several privacy-preserving approaches on facial images with different degree of privacy are proposed and evaluated on a deep neural network architecture for age estimation accuracy. We also introduce an attention mechanism which can adaptively learn discriminative features from the processed facial images. Experiments show that the proposed attention-based model performs better than the baseline model and achieves a reasonable performance to that with raw images in testing.