We present the Teacher-Student Generative Adversarial Network (TS-GAN) to generate depth images from single RGB images in order to boost the performance of face recognition systems. For our method to generalize well across unseen datasets, we design two components in the architecture, a teacher and a student. The teacher, which itself consists of a generator and a discriminator, learns a latent mapping between input RGB and paired depth images in a supervised fashion. The student, which consists of two generators (one shared with the teacher) and a discriminator, learns from new RGB data with no available paired depth information, for improved generalization. The fully trained shared generator can then be used in runtime to hallucinate depth from RGB for downstream applications such as face recognition. We perform rigorous experiments to show the superiority of TS-GAN over other methods in generating synthetic depth images. Moreover, face recognition experiments demonstrate that our hallucinated depth along with the input RGB images boosts performance across various architectures when compared to a single RGB modality by average values of +1.2%, +2.6%, and +2.6% for IIIT-D, EURECOM, and LFW datasets respectively. We make our implementation public at: https://github.com/hardik-uppal/teacher-student-gan.git.
翻译:我们展示教师-学生基因反差网络(TS-GAN),从单一的 RGB 图像中生成深度图像,以提高面部识别系统的性能。为了在不可见的数据集中全面推广我们的方法,我们设计了建筑结构的两个组成部分,即教师和学生。教师本身由一位发音者和一位歧视者组成,他们以监督的方式在输入的 RGB 和对齐深度图像之间学习潜在的映像图。学生由两台发音器(一个与教师共享)和一位导师组成,他们学习了新的 RGB 数据,没有对齐的深度信息,以便改进面部识别系统。然后,经过充分培训的共享生成器可以在运行时用于从 RGB 到下游应用的致幻深度,例如面识别。我们进行了严格的实验,以展示TS-GAN优于生成合成深度图像的其他方法之上。此外,面部识别实验表明,我们的深度与输入RGB 图像的深度一致,以及各种结构的提升性能提高性,而与单一的 RGB 模式相比,平均值为 +1.2%、+2.6% COM-qrb/rbs, 分别在公共数据中进行。