Learning privacy-enhancing face representations through feature disentanglement
| Autor: | Blaz Bortolato, Vitomir Struc, Marija Ivanovska, Peter Rot, Peter Peer, Janez Krizaj, Philipp Terhorst, Naser Damer |
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| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies Information privacy Artificial neural network Computer science business.industry 0211 other engineering and technologies Pattern recognition 02 engineering and technology Convolutional neural network Facial recognition system Discriminative model Face (geometry) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Identity (object-oriented programming) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | FG |
| DOI: | 10.1109/fg47880.2020.00007 |
| Popis: | Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art. |
| Databáze: | OpenAIRE |
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