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Communication Dans Un Congrès Année : 2021

Unsupervised learning of co-occurrences for face images retrieval

Résumé

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.
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Dates et versions

hal-03339397 , version 1 (09-09-2021)

Identifiants

Citer

Thomas Petit, Pierre Letessier, Stefan Duffner, Christophe Garcia. Unsupervised learning of co-occurrences for face images retrieval. MMAsia '20: ACM Multimedia Asia, Mar 2021, Virtual Event Singapore, Singapore. pp.1-7, ⟨10.1145/3444685.3446265⟩. ⟨hal-03339397⟩
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