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Face verification remains a challenging problem in very complex conditionswith large variations such as pose, illumination, expression, and occlusions.This problem is exacerbated when we rely unrealistically on a single trainingdata source, which is often insufficient to cover the intrinsically complexface variations. This paper proposes a principled multi-task learning approachbased on Discriminative Gaussian Process Latent Variable Model, namedGaussianFace, to enrich the diversity of training data. In comparison toexisting methods, our model exploits additional data from multiplesource-domains to improve the generalization performance of face verificationin an unknown target-domain. Importantly, our model can adapt automatically tocomplex data distributions, and therefore can well capture complex facevariations inherent in multiple sources. Extensive experiments demonstrate theeffectiveness of the proposed model in learning from diverse data sources andgeneralize to unseen domain. Specifically, the accuracy of our algorithmachieves an impressive accuracy rate of 98.52% on the well-known andchallenging Labeled Faces in the Wild (LFW) benchmark. For the first time, thehuman-level performance in face verification (97.53%) on LFW is surpassed.

Surpassing Human-Level Face Verification Performance on LFW with GaussianFace