Deep convolutional neural networks (CNNs) based approaches are the
state-of-the-art in various computer vision tasks, including face recognition.
Considerable research effort is currently being directed towards further
improving deep CNNs by focusing on more powerful model architectures and better
learning techniques. However, studies systematically exploring the strengths
and weaknesses of existing deep models for face recognition are still
relatively scarce in the literature. In this paper, we try to fill this gap and
study the effects of different covariates on the verification performance of
four recent deep CNN models using the Labeled Faces in the Wild (LFW) dataset.
Specifically, we investigate the influence of covariates related to: image
quality -- blur, JPEG compression, occlusion, noise, image brightness,
contrast, missing pixels; and model characteristics -- CNN architecture, color
information, descriptor computation; and analyze their impact on the face
verification performance of AlexNet, VGG-Face, GoogLeNet, and SqueezeNet. Based
on comprehensive and rigorous experimentation, we identify the strengths and
weaknesses of the deep learning models, and present key areas for potential
future research. Our results indicate that high levels of noise, blur, missing
pixels, and brightness have a detrimental effect on the verification
performance of all models, whereas the impact of contrast changes and
compression artifacts is limited. It has been found that the descriptor
computation strategy and color information does not have a significant
influence on performance.

本文通过使用野生标记人脸数据集（LFW）, 系统地研究了四种不同的深度卷积神经网络模型在面部识别任务中对图像质量、颜色信息、CNN 架构和描述符计算等不同协变量因素的影响，结果发现高噪声、模糊、缺失像素以及亮度对所有模型的识别准确性有不利影响，而对比度变化和压缩失真的影响有限，但同时发现描述符计算和颜色信息对性能没有显著影响，这对未来深度学习模型的进一步优化和改进提供了启示。