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In recent years, due to the emergence of deep learning, face recognition hasachieved exceptional success. However, many of these deep face recognitionmodels perform relatively poorly in handling profile faces compared to frontalfaces. The major reason for this poor performance is that it is inherentlydifficult to learn large pose invariant deep representations that are usefulfor profile face recognition. In this paper, we hypothesize that the profileface domain possesses a gradual connection with the frontal face domain in thedeep feature space. We look to exploit this connection by projecting theprofile faces and frontal faces into a common latent space and performverification or retrieval in the latent domain. We leverage a coupledgenerative adversarial network (cpGAN) structure to find the hiddenrelationship between the profile and frontal images in a latent commonembedding subspace. Specifically, the cpGAN framework consists of two GAN-basedsub-networks, one dedicated to the frontal domain and the other dedicated tothe profile domain. Each sub-network tends to find a projection that maximizesthe pair-wise correlation between two feature domains in a common embeddingfeature subspace. The efficacy of our approach compared with thestate-of-the-art is demonstrated using the CFP, CMU MultiPIE, IJB-A, and IJB-Cdatasets.

PF-cpGAN: Profile to Frontal Coupled GAN for Face Recognition in the Wild