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- W2898894585 abstract "In this paper, two face de-identification methods are proposed regarding face identification hindering against a deep neural network. Our work focuses on achieving a delicate balance, so that the facial images are miss-classified by the deep network, while the human observer can still identify the persons depicted in a scene. The proposed methods are based on achieving face de-identification by partly degrading image quality in order to hinder face recognition from deep neural networks, while maintaining the highest possible image quality, at the same time. To this end, we employ de-identification methods based on singular value decomposition and image hypersphere projections, respectively. From the conducted experiments, it can be concluded that these methods are capable of reducing correct face identification rates of the VGG-face network by over 90 %. Moreover, it is shown that these error rates preserve adequate image quality as is demonstrated through the values of the complex wavelet structural similarity index, allowing face recognition by humans contrary to most face de-identification methods." @default.
- W2898894585 created "2018-11-09" @default.
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- W2898894585 date "2018-09-01" @default.
- W2898894585 modified "2023-09-25" @default.
- W2898894585 title "QUALITY PRESERVING FACE DE-IDENTIFICATION AGAINST DEEP CNNS" @default.
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- W2898894585 doi "https://doi.org/10.1109/mlsp.2018.8517056" @default.
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