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 [1] 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 [2] , this paper additionally presents (1) analysis of a face recogniser (DeepID2+ [3] ), (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.

中文翻译：

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个人照片集中的人物识别


如今，人们通过社交媒体分享大部分个人生活。能够自动识别个人照片中的人物可以通过简化相册组织来极大地提高用户的便利性。然而，对于人类识别任务，计算机视觉的传统焦点一直是人脸识别和行人重新识别。社交媒体照片中的人物识别给计算机视觉带来了新的挑战，包括非合作主体（例如，向后的观点、不寻常的姿势）和外观的巨大变化。为了解决这个问题，我们构建了一个简单的人物识别框架，该框架利用来自多个图像区域（头部、身体等）的卷积网络特征。我们提出了新的识别场景，重点关注训练样本和测试样本之间的时间和外观差距。我们根据时间和观点的普遍性对不同特征的重要性进行了深入分析。在此过程中，我们验证了我们的简单方法在 PIPA [1] 基准上达到了最先进的结果，PIPA [1] 基准可以说是迄今为止最大的基于社交媒体的人物识别基准，具有不同的姿势、观点、社会群体和事件。与会议版本的论文[2]相比，本文额外提出了（1）人脸识别器（DeepID2+[3]）的分析，（2）新方法naeil2，结合了会议版本方法naeil和DeepID2+以实现人脸识别器的状态艺术结果甚至与会议后的作品进行比较，（3）自会议版本以来相关工作的讨论，（4）附加分析，包括头部视点的性能细分，以及（5）开放世界设置的结果。