论文标题
在差异变体检测的分离表示上的共同信息最大化
Mutual Information Maximization on Disentangled Representations for Differential Morph Detection
论文作者
论文摘要
在本文中，我们提出了一个新型的差异变体检测框架，利用具有里程碑意义和外观分离。在我们的框架中，面部图像在嵌入域中使用两个分离但互补的表示形式表示。该网络受面部图像的三联体训练，其中中间图像从一个图像中继承了地标和另一个图像的外观。使用对比表示，该最初训练的网络将进一步为每个数据集培训。我们证明，通过采用外观和地标脱离，拟议的框架可以提供最新的差异变体检测性能。该功能是通过地标，外观和ID域中的使用距离来实现的。使用不同方法生成的三个Morph数据集评估了所提出的框架的性能。
In this paper, we present a novel differential morph detection framework, utilizing landmark and appearance disentanglement. In our framework, the face image is represented in the embedding domain using two disentangled but complementary representations. The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image. This initially trained network is further trained for each dataset using contrastive representations. We demonstrate that, by employing appearance and landmark disentanglement, the proposed framework can provide state-of-the-art differential morph detection performance. This functionality is achieved by the using distances in landmark, appearance, and ID domains. The performance of the proposed framework is evaluated using three morph datasets generated with different methodologies.
