论文标题
阴影可以揭示生物识别信息吗？
Can Shadows Reveal Biometric Information?
论文作者
论文摘要
我们通过查看在弥漫表面上铸造的对象的阴影来研究个体的生物特征信息的问题。我们表明，通过最大似然分析，在代表性的情况下，阴影中的生物特征信息泄漏可以足够用于可靠的身份推断。然后，我们开发了一种基于学习的方法，该方法在实际设置中证明了这种现象，从而利用阴影中的微妙提示是泄漏源的根源，而无需任何标记的真实数据。特别是，我们的方法依赖于构建由从每个身份的单个照片获得的3D面模型组成的合成场景。我们以完全无监督的方式将我们从合成数据中学到的知识转移到真实数据中。我们的模型能够很好地概括到真实的领域，并且在场景中的几种变体都有鲁棒性。我们在身份分类任务中报告了高分类精度，该任务发生在具有未知几何形状和遮挡对象的场景中。
We study the problem of extracting biometric information of individuals by looking at shadows of objects cast on diffuse surfaces. We show that the biometric information leakage from shadows can be sufficient for reliable identity inference under representative scenarios via a maximum likelihood analysis. We then develop a learning-based method that demonstrates this phenomenon in real settings, exploiting the subtle cues in the shadows that are the source of the leakage without requiring any labeled real data. In particular, our approach relies on building synthetic scenes composed of 3D face models obtained from a single photograph of each identity. We transfer what we learn from the synthetic data to the real data using domain adaptation in a completely unsupervised way. Our model is able to generalize well to the real domain and is robust to several variations in the scenes. We report high classification accuracies in an identity classification task that takes place in a scene with unknown geometry and occluding objects.
