{"title":"我认识那个人:图像中人的生成全身和面部去认同","authors":"K. Brkić, I. Sikirić, T. Hrkać, Z. Kalafatić","doi":"10.1109/CVPRW.2017.173","DOIUrl":null,"url":null,"abstract":"We propose a model for full body and face deidentification of humans in images. Given a segmentation of the human figure, our model generates a synthetic human image with an alternative appearance that looks natural and fits the segmentation outline. The model is usable with various levels of segmentation, from simple human figure blobs to complex garment-level segmentations. The level of detail in the de-identified output depends on the level of detail in the input segmentation. The model de-identifies not only primary biometric identifiers (e.g. the face), but also soft and non-biometric identifiers including clothing, hairstyle, etc. Quantitative and perceptual experiments indicate that our model produces de-identified outputs that thwart human and machine recognition, while preserving data utility and naturalness.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"10 1","pages":"1319-1328"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"77","resultStr":"{\"title\":\"I Know That Person: Generative Full Body and Face De-identification of People in Images\",\"authors\":\"K. Brkić, I. Sikirić, T. Hrkać, Z. Kalafatić\",\"doi\":\"10.1109/CVPRW.2017.173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a model for full body and face deidentification of humans in images. Given a segmentation of the human figure, our model generates a synthetic human image with an alternative appearance that looks natural and fits the segmentation outline. The model is usable with various levels of segmentation, from simple human figure blobs to complex garment-level segmentations. The level of detail in the de-identified output depends on the level of detail in the input segmentation. The model de-identifies not only primary biometric identifiers (e.g. the face), but also soft and non-biometric identifiers including clothing, hairstyle, etc. Quantitative and perceptual experiments indicate that our model produces de-identified outputs that thwart human and machine recognition, while preserving data utility and naturalness.\",\"PeriodicalId\":6668,\"journal\":{\"name\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"volume\":\"10 1\",\"pages\":\"1319-1328\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"77\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPRW.2017.173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2017.173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
I Know That Person: Generative Full Body and Face De-identification of People in Images
We propose a model for full body and face deidentification of humans in images. Given a segmentation of the human figure, our model generates a synthetic human image with an alternative appearance that looks natural and fits the segmentation outline. The model is usable with various levels of segmentation, from simple human figure blobs to complex garment-level segmentations. The level of detail in the de-identified output depends on the level of detail in the input segmentation. The model de-identifies not only primary biometric identifiers (e.g. the face), but also soft and non-biometric identifiers including clothing, hairstyle, etc. Quantitative and perceptual experiments indicate that our model produces de-identified outputs that thwart human and machine recognition, while preserving data utility and naturalness.