Sequential person recognition in photo albums with a recurrent network

Yao Li, Guosheng Lin, Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Anton van den Hengel

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

24 Citations (Scopus)


Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to issues such as non-frontal faces, changes in clothing, location and lighting. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances' labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.

Original languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
EditorsYanxi Liu, James M. Rehg, Camillo J. Taylor, Ying Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781538604571
ISBN (Print)9781538604588
Publication statusPublished - 2017
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2017 - Honolulu, United States of America
Duration: 21 Jul 201726 Jul 2017 (Proceedings)


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2017
Abbreviated titleCVPR 2017
Country/TerritoryUnited States of America
Internet address

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