Negative Face Recognition, or NFR, is a face recognition approach that enhances the soft-biometric privacy on the template-level by representing face templates in a complementary (negative) domain. While ordinary templates characterize facial properties of an individual, negative templates describe facial properties that does not exist for this individual. This suppresses privacy-sensitive information from stored templates. Experiments are conducted on two publicly available datasets captured under controlled and uncontrolled scenarios on three privacy-sensitive attributes.
Source: Unsupervised Enhancement of Soft-biometric Privacy with Negative Face RecognitionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Classification | 1 | 12.50% |
Zero-Shot Learning | 1 | 12.50% |
3D Reconstruction | 1 | 12.50% |
Machine Translation | 1 | 12.50% |
NMT | 1 | 12.50% |
Sentence | 1 | 12.50% |
Translation | 1 | 12.50% |
Face Recognition | 1 | 12.50% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |