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Automatic Face Naming with Caption-based Supervision

Matthieu Guillaumin 1 Thomas Mensink 1 Jakob Verbeek 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We consider two scenarios of naming people in databases of news photos with captions: (i) finding faces of a single person, and (ii) assigning names to all faces. We combine an initial text-based step, that restricts the name assigned to a face to the set of names appearing in the caption, with a second step that analyzes visual features of faces. By searching for groups of highly similar faces that can be associated with a name, the results of purely text-based search can be greatly ameliorated. We improve a recent graph-based approach, in which nodes correspond to faces and edges connect highly similar faces. We introduce constraints when optimizing the objective function, and propose improvements in the low-level methods used to construct the graphs. Furthermore, we generalize the graph-based approach to face naming in the full data set. In this multi-person naming case the optimization quickly becomes computationally demanding, and we present an important speed-up using graph-flows to compute the optimal name assignments in documents. Generative models have previously been proposed to solve the multi-person naming task. We compare the generative and graph-based methods in both scenarios, and find significantly better performance using the graph-based methods in both cases.
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Submitted on : Monday, April 11, 2011 - 2:28:47 PM
Last modification on : Thursday, January 20, 2022 - 5:30:57 PM
Long-term archiving on: : Saturday, December 3, 2016 - 11:12:32 PM




Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek, Cordelia Schmid. Automatic Face Naming with Caption-based Supervision. CVPR 2008 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2008, Anchorage, United States. pp.1-8, ⟨10.1109/CVPR.2008.4587603⟩. ⟨inria-00321048v2⟩



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