  In recent years, with the advent of deep-learning, face recognition has
achieved exceptional success. However, many of these deep face recognition
models perform much better in handling frontal faces compared to profile faces.
The major reason for poor performance in handling of profile faces is that it
is inherently difficult to learn pose-invariant deep representations that are
useful for profile face recognition. In this paper, we hypothesize that the
profile face domain possesses a latent connection with the frontal face domain
in a latent feature subspace. We look to exploit this latent connection by
projecting the profile faces and frontal faces into a common latent subspace
and perform verification or retrieval in the latent domain. We leverage a
coupled conditional generative adversarial network (cpGAN) structure to find
the hidden relationship between the profile and frontal images in a latent
common embedding subspace. Specifically, the cpGAN framework consists of two
conditional GAN-based sub-networks, one dedicated to the frontal domain and the
other dedicated to the profile domain. Each sub-network tends to find a
projection that maximizes the pair-wise correlation between the two feature
domains in a common embedding feature subspace. The efficacy of our approach
compared with the state-of-the-art is demonstrated using the CFP, CMU
Multi-PIE, IJB-A, and IJB-C datasets. Additionally, we have also implemented a
coupled convolutional neural network (cpCNN) and an adversarial discriminative
domain adaptation network (ADDA) for profile to frontal face recognition. We
have evaluated the performance of cpCNN and ADDA and compared it with the
proposed cpGAN. Finally, we have also evaluated our cpGAN for reconstruction of
frontal faces from input profile faces contained in the VGGFace2 dataset.
