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.

本文提出了一种基于区分性高斯过程潜在变量模型的多任务学习方法-GaussianFace，旨在丰富训练数据的多样性以改进在未知目标域中的人脸验证泛化能力，通过利用多源域的额外数据，实现自适应复杂数据分布以更好地捕获多种来源中的复杂人脸变化，并在通用人脸库Labeled Faces in the Wild (LFW)上取得了惊人的98.52％准确率，首次超越了人类的97.53％。

使用GaussianFace在LFW上超越人类级别的人脸验证表现