Name
Abstract | ||
|---|---|---|
ABSTRACTWith the popularity of using computer vision technology in monitoring system, there is an increasing societal concern on intruding people's privacy as the captured images/videos may contain identity-related information e.g. people's face. Existing methods on protecting such privacy focus on removing the identity-related information from faces. However, this would weaken the utility of current monitoring system. In this paper, we develop a face anonymization framework that could obfuscate visual appearance while preserving the identity discriminability. The framework is composed of two parts: an identity-aware region discovery module and an identity-aware face confusion module. The former adaptively locates the identity-independent attributes on human faces, and the latter generates the privacy-preserving faces using original faces and discovered facial attributes. To optimize the face generator, we employ a multi-task based loss function, which consists of discriminator loss, identify preserving loss, and reconstruction loss functions. Our model can achieve a balance between recognition utility and appearance anonymizing by modifying different numbers of facial attributes according to pratical demands, and provide a variety of results. Extensive experiments conducted on two public benchmarks Celeb-A and VGG-Face2 demonstrate the effectiveness of our model under distinct face recognition scenarios. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3474085.3475367 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingzhi Li | 1 | 2 | 1.37 |
| Lutong Han | 2 | 1 | 0.35 |
| Ruoyu Chen | 3 | 1 | 0.35 |
| Hua Zhang | 4 | 1 | 0.35 |
| Bing Han | 5 | 36 | 11.27 |
| Lili Wang | 6 | 172 | 45.30 |
| Xiaochun Cao | 7 | 1986 | 131.55 |