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Investigating Nuisance Factors in Face Recognition with DCNN Representation

机译：用DCNN代表研究人脸识别因素

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Deep learning based approaches proved to be dramatically effective to address many computer vision applications, including "face recognition in the wild". It has been extensively demonstrated that methods exploiting Deep Convolutional Neural Networks (DCNN) are powerful enough to overcome to a great extent many problems that negatively affected computer vision algorithms based on hand-crafted features. These problems include variations in illumination, pose, expression and occlusion, to mention some. The DCNNs excellent discriminative power comes from the fact that they learn low-and high-level representations directly from the raw image data. Considering this, it can be assumed that the performance of a DCNN are influenced by the characteristics of the raw image data that are fed to the network. In this work, we evaluate the effect of different bounding box dimensions, alignment, positioning and data source on face recognition using DCNNs, and present a thorough evaluation on two well known, public DCNN architectures.
机译：基于深度学习的方法被证明是显着的有效地解决了许多计算机视觉应用，包括“野外的人脸识别”。它已被广泛证明利用深度卷积神经网络（DCNN）的方法足够强大，可以在很大程度上克服基于手工制作的特征对计算机视觉算法产生负面影响的许多问题。这些问题包括照明，姿势，表达和闭塞的变化，提及一些。 DCNN卓越的歧视力来自于他们直接从原始图像数据学习低和高级表示。考虑到这一点，可以假设DCNN的性能受到馈送到网络的原始图像数据的特征的影响。在这项工作中，我们使用DCNNS评估不同边界框尺寸，对准，定位和数据源对面部识别的影响，并对两个众所周知的公共DCNN架构进行全面评估。

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