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