Due to the prevalence of social media websites, one challenge facing computer
vision researchers is to devise methods to process and search for persons of
interest among the billions of shared photos on these websites. Facebook
revealed in a 2013 white paper that its users have uploaded more than 250
billion photos, and are uploading 350 million new photos each day. Due to this
humongous amount of data, large-scale face search for mining web images is both
important and challenging. Despite significant progress in face recognition,
searching a large collection of unconstrained face images has not been
adequately addressed. To address this challenge, we propose a face search
system which combines a fast search procedure, coupled with a state-of-the-art
commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe
face, we first filter the large gallery of photos to find the top-k most
similar faces using deep features generated from a convolutional neural
network. The k candidates are re-ranked by combining similarities from deep
features and the COTS matcher. We evaluate the proposed face search system on a
gallery containing 80 million web-downloaded face images. Experimental results
demonstrate that the deep features are competitive with state-of-the-art
methods on unconstrained face recognition benchmarks (LFW and IJB-A). Further,
the proposed face search system offers an excellent trade-off between accuracy
and scalability on datasets consisting of millions of images. Additionally, in
an experiment involving searching for face images of the Tsarnaev brothers,
convicted of the Boston Marathon bombing, the proposed face search system could
find the younger brother's (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a
5M gallery and at rank 8 in 7 seconds on an 80M gallery.

本文提出了一个脸部搜索系统，它使用快速搜索程序和 COTS 匹配器相结合的级联框架，通过卷积神经网络生成的深度特征来过滤大量的照片，并在 80 百万张网络下载的人脸图像的图库上对该系统进行了评估。