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  Person Recognition in Social Media Photos

Oh, S. J., Benenson, R., Fritz, M., & Schiele, B. (2017). Person Recognition in Social Media Photos. Retrieved from http://arxiv.org/abs/1710.03224.

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 Creators:
Oh, Seong Joon1, Author              
Benenson, Rodrigo1, Author              
Fritz, Mario1, Author              
Schiele, Bernt1, Author              
Affiliations:
1Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 Abstract: 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.

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Language(s): eng - English
 Dates: 2017-10-092017
 Publication Status: Published online
 Pages: 16 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1710.03224
URI: http://arxiv.org/abs/1710.03224
BibTex Citekey: Oh1710.03224
 Degree: -

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