Color Transfer to Anonymized Gait Images While Maintaining Anonymization
Color Transfer to Anonymized Gait Images While Maintaining Anonymization
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发表时间：
2020-12
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通讯作者：
Ngoc-Dung T. Tieu;J. Yamagishi;I. Echizen
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作者：
Ngoc-Dung T. Tieu;J. Yamagishi;I. Echizen
步态匿名化有助于防止步态识别系统使用上传到社交媒体的视频识别人。我们目前的步态匿名化方法是首先修改步态序列的轮廓，然后将原始步态图像中的皮肤，头发，衣服等颜色转移到修改后的步态图像中，以产生最终的RGB匿名步态图像序列。由于用户通常关心生成的视频的质量，因为他们除了关心隐私之外还想与家人和朋友分享，因此生成的视频应该包含清晰且纹理精细的彩色图像。现有的匿名化模型无法生成这样的图像。在本文中，我们专注于颜色转移，同时保持匿名化。这是具有挑战性的，因为原始步态图像可以由多种颜色组成，修改的步态不同于原始步态，不存在真实的地面实况匿名化步态，并且可能难以将原始步态图像中的前景颜色与背景颜色精确地分离。为了克服这个问题，我们建议在不使用地面实况和不提取原始步态图像中的颜色的情况下传输颜色。在该模型中，首先定位两种步态之间的重叠区域，并将原始图像中该区域的颜色转移到修改后的图像中。剩余区域中的颜色是从重叠区域中的颜色插值的，因此重叠区域和非重叠区域中的颜色是连贯的。定量和定性的实验表明，该模型是更有效的比我们以前的模型，没有减少匿名。
Gait anonymization helps prevent the identification of people by gait recognition systems using videos uploaded to social media. Our current gait anonymization approach is to first modify the silhouette of the gait sequence and then transfer the colors of the skin, hair, clothing, etc. in the original gait images to the modified gait images to produce a final RGB anonymized gait image sequence. Since users typically care about the quality of the generated videos as they want to share them with family and friends in addition to caring about privacy, the generated videos should contain color images that are sharp and finely textured. Existing anonymization models are unable to produce such images. In this paper, we focus on color transfer while maintaining anonymization. This is challenging because the original gait images may consist of multiple colors, the modified gait differs from the original one, there is no real ground truth anonymized gait, and it may be difficult to exactly separate the foreground colors from the background colors in the original gait images. To overcome this problem, we propose transferring the colors without using ground truth and without extracting the colors in the original gait images. In this model, the overlapping region between the two gaits is first located, and the colors in that region in the original images are transferred to the modified images. The colors in the remaining region are interpolated from the color in the overlapping region, so the colors in the overlapping and non-overlapping regions are coherent. Quantitative and qualitative experiments demonstrated that the proposed model is more effective than our previous models with no reduction in anonymization.