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Unsupervised learning of co-occurrences for face images retrieval

Unsupervised learning of co-occurrences for face images retrieval

Authors :
Christophe Garcia
Thomas Petit
Stefan Duffner
Pierre Letessier
Institut National de l'Audiovisuel (INA)
Extraction de Caractéristiques et Identification (imagine)
Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS)
Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)
Université de Lyon-École Centrale de Lyon (ECL)
Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Université de Lyon-Université Lumière - Lyon 2 (UL2)
Source :
MMAsia '20: Proceedings of the 2nd ACM International Conference on Multimedia in Asia, MMAsia '20: ACM Multimedia Asia, MMAsia '20: ACM Multimedia Asia, Mar 2021, Virtual Event Singapore, Singapore. pp.1-7, ⟨10.1145/3444685.3446265⟩
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

International audience; Despite a huge leap in performance of face recognition systems in recent years, some cases remain challenging for them while being trivial for humans. This is because a human brain is exploiting much more information than the face appearance to identify a person. In this work, we aim at capturing the social context of unlabeled observed faces in order to improve face retrieval. In particular, we propose a framework that substantially improves face retrieval by exploiting the faces occurring simultaneously in a query's context to infer a multi-dimensional social context descriptor. Combining this compact structural descriptor with the individual visual face features in a common feature vector considerably increases the correct face retrieval rate and allows to disambiguate a large proportion of query results of different persons that are barely distinguishable visually. To evaluate our framework, we also introduce a new large dataset of faces of French TV personalities organised in TV shows in order to capture the co-occurrence relations between people. On this dataset, our framework is able to improve the mean Average Precision over a set of internal queries from 67.93% (using only facial features extracted with a state-of-the-art pre-trained model) to 78.16% (using both facial features and faces co-occurrences), and from 67.88% to 77.36% over a set of external queries.

Details

Language :
English
Database :
OpenAIRE
Journal :
MMAsia '20: Proceedings of the 2nd ACM International Conference on Multimedia in Asia, MMAsia '20: ACM Multimedia Asia, MMAsia '20: ACM Multimedia Asia, Mar 2021, Virtual Event Singapore, Singapore. pp.1-7, ⟨10.1145/3444685.3446265⟩
Accession number :
edsair.doi.dedup.....4e3050f8c823107c22555a2f96abab79
Full Text :
https://doi.org/10.1145/3444685.3446265⟩