MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
Yandong Guo Lei Zhang Yuxiao Hu Xiaodong He Jianfeng Gao
Abstract
In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains $10$ M images in version $1$ , is the largest publicly available one in the world.
中文速览
人脸识别领域长期缺乏一个既能回答"这是谁"又能消歧的大规模公开基准，现有公开数据集规模远小于工业界私有数据。为此，研究者提出了一个从百万名公众人物人脸图像中识别身份、并将结果链接到知识库（Freebase）唯一实体键的基准任务，通过知识库中的丰富属性信息天然解决同名歧义问题。他们爬取并整理了包含1000万张图像、覆盖10万顶级名人的训练集（当时最大的公开人脸训练数据），并配套设计了含干扰图像的人工标注评测集和评估协议。在此基础上训练卷积神经网络分类模型，基线结果在精确率95%的严格条件下识别覆盖率达44.2%。这项工作不仅将人脸识别推向计算机视觉中规模最大的分类问题之一，也为学术界提供了可公开获取的大规模训练与评测资源，对图像描述生成、新闻视频理解等实际应用具有直接价值。
原文 arXiv:1607.08221;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1607.08221v1