People nowadays share large parts of their personal lives through social
media. Being able to automatically recognise people in personal photos may
greatly enhance user convenience by easing photo album organisation. For human
identification task, however, traditional focus of computer vision has been
face recognition and pedestrian re-identification. Person recognition in social
media photos sets new challenges for computer vision, including non-cooperative
subjects (e.g. backward viewpoints, unusual poses) and great changes in
appearance. To tackle this problem, we build a simple person recognition
framework that leverages convnet features from multiple image regions (head,
body, etc.). We propose new recognition scenarios that focus on the time and
appearance gap between training and testing samples. We present an in-depth
analysis of the importance of different features according to time and
viewpoint generalisability. In the process, we verify that our simple approach
achieves the state of the art result on the PIPA benchmark, arguably the
largest social media based benchmark for person recognition to date with
diverse poses, viewpoints, social groups, and events.
Compared the conference version of the paper, this paper additionally
presents (1) analysis of a face recogniser (DeepID2+), (2) new method naeil2
that combines the conference version method naeil and DeepID2+ to achieve state
of the art results even compared to post-conference works, (3) discussion of
related work since the conference version, (4) additional analysis including
the head viewpoint-wise breakdown of performance, and (5) results on the
open-world setup.

该论文提出了一种基于多图像区域（头部、身体等）的简单的人员识别框架，以应对社交媒体照片中的人员识别问题，并针对训练和测试样本之间的时间和外观差距提出了新的识别方法，该方法在 PIPA 基准上取得了最先进的结果，对不同的特征进行了深入的分析。