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期刊论文

Improving face recognition with domain adaptation

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Neurocomputing,2018,287():45-51 | 2018年04月26日 | doi.org/10.1016/j.neucom.2018.01.079

URL:https://www.sciencedirect.com/science/article/abs/pii/S0925231218301127?via%3Dihub

摘要/描述

Nearly all recent face recognition algorithms have been evaluated on the Labeled Faces in the Wild (LFW) dataset and many of them achieved over 99% accuracy. However, the performance is still not enough for real-world applications. One problem is the data bias. The faces in LFW and other web-collected datasets come from celebrities. They are quite different from the faces of a normal person captured in the daily life. In other words, they are different in the face distribution. Replacing the training data with the same distribution is a simple solution. However, the photos of common people are much harder to collect because of the privacy concerns. So it is useful to develop a method that transfers the knowledge in the data of different face distribution to help improving the final performance. In this paper, we crawl a large face dataset whose distribution is different from LFW and show the improvement of LFW accuracy with a simple domain adaptation technique. To the best of our knowledge, it is the first time that domain adaptation is applied in the unconstrained face recognition problem with a million scale dataset. Besides, we incorporate face verification threshold into FaceNet triplet loss function explicitly. Finally, we achieve 99.33% on the LFW benchmark with only single CNN model and similar performance even without face alignment.

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