Phillips P Jonathon, Flynn Patrick J, Bowyer Kevin W
National Institute of Standards and Technology, 100 Bureau Drive MS 8490, Gaithersburg, MD 20899, USA.
Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
Image Vis Comput. 2017 Feb;58. doi: 10.1016/j.imavis.2016.08.004.
Over the past decade, independent evaluations have become commonplace in many areas of experimental computer science, including face and gesture recognition. A key attribute of many successful independent evaluations is a curated data set. Desired aspects associated with these data sets include appropriateness to the experimental design, a corpus size large enough to allow statistically rigorous characterization of results, and the availability of comprehensive metadata that allow inferences to be made on various data set attributes. In this paper, we review a ten-year biometric sampling effort that enabled the creation of several key biometrics challenge problems. We summarize the design and execution of data collections, identify key challenges, and convey some lessons learned.
在过去十年中，独立评估在实验计算机科学的许多领域已变得司空见惯，包括面部和手势识别。许多成功的独立评估的一个关键属性是经过精心策划的数据集。与这些数据集相关的期望方面包括适合实验设计、语料库大小足够大以允许对结果进行严格的统计表征，以及提供全面的元数据以便能够对各种数据集属性进行推断。在本文中，我们回顾了一项为期十年的生物特征采样工作，该工作促成了几个关键生物特征挑战问题的产生。我们总结了数据收集的设计与执行情况，识别了关键挑战，并分享了一些经验教训。
