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State-of-the-art face recognition performance using publicly available software and datasets

机译：使用公开可用的软件和数据集的最新人脸识别性能

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We are interested in the reproducibility of face recognition systems. By reproducibility we mean: is the scientific community, and are the researchers from different sides, capable of reproducing the last published results by a big company, that has at its disposal huge computational power and huge proprietary databases? With the constant advancements in GPU computation power and availability of open-source software, the reproducibility of published results should not be a problem. But, if architectures of the systems are private and databases are proprietary, the reproducibility of published results can not be easily attained. To tackle this problem, we focus on training and evaluation of face recognition systems on publicly available data and software. We are also interested in comparing the best Deep Neural Net (DNN) based results with a baseline “classical” system. This paper exploits the OpenFace open-source system to generate a deep convolutional neural network model using publicly available datasets. We study the impact of the size of the datasets, their quality and compare the performance to a classical face recognition approach. Our focus is to have a fully reproducible model. To this end, we used publicly available datasets (FRGC, MS-celeb-lM, MOBIO, LFW), as well publicly available software (OpenFace) to train our model in order to do face recognition. Our best trained model achieves 97.52% accuracy on the Labelled in the Wild dataset (LFW) dataset which is lower than Google's best reported results of 99.96% but slightly better than FaceBook's reported result of 97.35%. We also evaluated our best model on the challenging video dataset MOBIO and report competitive results with the best reported results on this database.
机译：我们对人脸识别系统的可重复性感兴趣。通过可再现性，我们的意思是：科学界和来自不同方面的研究人员是否能够复制拥有巨大计算能力和庞大专有数据库的大公司的最新发表的结果？随着GPU计算能力的不断提高和开放源代码软件的可用性，已发布结果的可重复性应该不会成为问题。但是，如果系统的体系结构是私有的，而数据库是专有的，那么发布结果的可重现性就很难实现。为了解决这个问题，我们专注于在公开数据和软件上对人脸识别系统进行培训和评估。我们也有兴趣将基于最佳深度神经网络（DNN）的结果与基准“经典”系统进行比较。本文利用OpenFace开源系统使用公开可用的数据集生成深度卷积神经网络模型。我们研究了数据集大小，质量的影响，并将其性能与经典的人脸识别方法进行了比较。我们的重点是建立一个完全可复制的模型。为此，我们使用了公开可用的数据集（FRGC，MS-celeb-LM，MOBIO，LFW）以及公开可用的软件（OpenFace）来训练我们的模型以进行人脸识别。我们训练有素的模型在野外数据集（LFW）数据集上达到了97.52％的准确率，这比Google最佳报告的99.96％的结果要低，但略好于FaceBook的97.35％的结果。我们还在具有挑战性的视频数据集MOBIO上评估了最佳模型，并在此数据库上报告了最佳结果并报告了竞争结果。

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