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In recent years, with the advent of deep-learning, face recognition hasachieved exceptional success. However, many of these deep face recognitionmodels perform much better in handling frontal faces compared to profile faces.The major reason for poor performance in handling of profile faces is that itis inherently difficult to learn pose-invariant deep representations that areuseful for profile face recognition. In this paper, we hypothesize that theprofile face domain possesses a latent connection with the frontal face domainin a latent feature subspace. We look to exploit this latent connection byprojecting the profile faces and frontal faces into a common latent subspaceand perform verification or retrieval in the latent domain. We leverage acoupled conditional generative adversarial network (cpGAN) structure to findthe hidden relationship between the profile and frontal images in a latentcommon embedding subspace. Specifically, the cpGAN framework consists of twoconditional GAN-based sub-networks, one dedicated to the frontal domain and theother dedicated to the profile domain. Each sub-network tends to find aprojection that maximizes the pair-wise correlation between the two featuredomains in a common embedding feature subspace. The efficacy of our approachcompared with the state-of-the-art is demonstrated using the CFP, CMUMulti-PIE, IJB-A, and IJB-C datasets. Additionally, we have also implemented acoupled convolutional neural network (cpCNN) and an adversarial discriminativedomain adaptation network (ADDA) for profile to frontal face recognition. Wehave evaluated the performance of cpCNN and ADDA and compared it with theproposed cpGAN. Finally, we have also evaluated our cpGAN for reconstruction offrontal faces from input profile faces contained in the VGGFace2 dataset.

Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN