仅用一张人脸图和大量无标签数据，实现高精度身份认证。
Learning from a single labeled face and a stream of unlabeled data
- 基于单张有标签图像与持续流入的无标签数据，构建非参数化人脸模型。
- 在43人数据集上实现90%识别率，误报接近零，比基线提升25%以上。
- 适用于手机/电脑摄像头等场景，无需负样本，适合实际部署。
单张图像识别个人是极具挑战性的问题，因训练样本极小。本文研究一种新设定：仅需识别一个人，且无其他人员的标注数据。该设定自然出现在个人电脑与移动设备的身份认证中，但因缺乏负样本而更具挑战性。我们将问题形式化为一类分类，并提出并分析了一种算法，利用单张有标签图像和持续流入的无标签数据学习人脸的非参数模型。在许多场景下（如用户通过摄像头与计算机交互），无标签数据丰富且易获取。这是首篇研究此类数据如何提升单图像身份识别性能的论文。我们在包含43人的数据集上评估，结果表明该方法可在几乎零误报的情况下实现90%的识别率，比最佳基线提升超过25%。最后，我们进行了全面的敏感性分析，并提供实用的参数设置指南。
原文摘要 · Abstract (English)
Face recognition from a single image per person is a challenging problem because the training sample is extremely small. We consider a variation of this problem. In our problem, we recognize only one person, and there are no labeled data for any other person. This setting naturally arises in authentication on personal computers and mobile devices, and poses additional challenges because it lacks negative examples. We formalize our problem as one-class classification, and propose and analyze an algorithm that learns a non-parametric model of the face from a single labeled image and a stream of unlabeled data. In many domains, for instance when a person interacts with a computer with a camera, unlabeled data are abundant and easy to utilize. This is the first paper that investigates how these data can help in learning better models in the single-image-per-person setting. Our method is evaluated on a dataset of 43 people and we show that these people can be recognized 90% of time at nearly zero false positives. This recall is 25+% higher than the recall of our best performing baseline. Finally, we conduct a comprehensive sensitivity analysis of our algorithm and provide a guideline for setting its parameters in practice.
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