Identity preservation is crucial for the practical application of face sketch and photo synthesis, such as law enforcement and entertainment. However, existing methods mainly focus on generating faces with good visual quality, leading the evaluation of identity preservation is partial and insufficient. Besides, it is difficult to simultaneously synthesize photos and sketches that have good visual quality and identity preservation. In this article, we propose to utilize auxiliary deep features extracted by an off-the-shelf face classifier to inject into the synthesis processes, so that the synthesized faces have more identity information. Furthermore, we propose a light interpolated convolutional neural network that has a shared encoder and two face-specialized decoders to simultaneously complete the transformations between sketches and photos. Evaluations on identity preservation and visual quality show our method is superior to existing methods in synthesizing both sketches and photos, and is qualified in practical application.

中文翻译：

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使用深度特征注入在草图和照片之间实现身份保留人脸合成


身份保存对于面部素描和照片合成的实际应用（例如执法和娱乐）至关重要。然而，现有方法主要侧重于生成具有良好视觉质量的人脸，导致身份保存的评估是片面的和不足的。此外，很难同时合成具有良好视觉质量和身份保存的照片和草图。在本文中，我们建议利用现成的人脸分类器提取的辅助深层特征注入到合成过程中，以便合成的人脸具有更多的身份信息。此外，我们提出了一种光插值卷积神经网络，它具有一个共享编码器和两个面部专用解码器，可以同时完成草图和照片之间的转换。身份保存和视觉质量的评估表明，我们的方法在合成草图和照片方面优于现有方法，并且在实际应用中是合格的。