I Know That Person: Generative Full Body and Face De-identification of People in Images

K. Brkić, I. Sikirić, T. Hrkać, Z. Kalafatić
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引用次数: 77

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.
我认识那个人:图像中人的生成全身和面部去认同
我们提出了一种人体和人脸图像去识别模型。给定人体的分割,我们的模型生成具有替代外观的合成人体图像,该图像看起来自然且符合分割轮廓。该模型可用于各种级别的分割,从简单的人体斑点到复杂的服装级别分割。去识别输出的细节程度取决于输入分割的细节程度。该模型不仅去识别主要的生物特征(如面部),还去识别软特征和非生物特征,如服装、发型等。定量和感知实验表明,我们的模型产生的去识别输出阻碍了人类和机器的识别,同时保留了数据的实用性和自然性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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