  We present the Teacher-Student Generative Adversarial Network (TS-GAN) to
generate depth images from a single RGB image in order to boost the recognition
accuracy of face recognition (FR) 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.
