Surpassing Human-Level Face Verification Performance on LFW with GaussianFace

Abstract

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.

Appearing in Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), Oral Presentation

References in corpus (1)

• Recover Canonical-View Faces in the Wild with Deep Neural Networks

Cited by in corpus (9)

• Deep Learning Face Representation by Joint Identification-Verification
• DeepID3: Face Recognition with Very Deep Neural Networks
• Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
• Recover Canonical-View Faces in the Wild with Deep Neural Networks
• Deeply learned face representations are sparse, selective, and robust
• Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
• Effective Face Frontalization in Unconstrained Images
• Learning Robust Deep Face Representation
• Spike and Slab Gaussian Process Latent Variable Models