Face recognition capabilities have recently made extraordinary leaps. Though this progress is at least partially due to ballooning training data sizes -- huge numbers of face images downloaded and labeled for identity -- it is not clear if the formidable task of collecting so many images is truly necessary. We propose a far more accessible means of increasing training data sizes for face recognition systems. Rather than manually harvesting and labeling more faces, we simply synthesize them. We describe novel methods of enriching an existing dataset with important facial appearance variations by manipulating the faces it contains. We further apply this synthesis approach when matching query images represented using a standard convolutional neural network. The effect of training and testing with synthesized images is extensively tested on the LFW and IJB-A (verification and identification) benchmarks and Janus CS2. The performances obtained by our approach match state of the art results reported by systems trained on millions of images.

提出使用面部合成的方法，而非手动标注更多面部图像，从而增加面部识别系统的训练数据，并将其应用于卷积神经网络，获得与数百万下载图像训练的系统报告的最新结果相匹配的性能表现。

有效的人脸识别，是否真的需要收集数百万张人脸？