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Unsupervised learning of co-occurrences for face images retrieval
Unsupervised learning of co-occurrences for face images retrieval
- 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.
- Subjects :
- Computer science
business.industry
Feature vector
Pattern recognition
Context (language use)
co-occurrences
02 engineering and technology
[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
Facial recognition system
facial recognition
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Face (geometry)
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
0202 electrical engineering, electronic engineering, information engineering
Unsupervised learning
020201 artificial intelligence & image processing
Person recognition
Document structure person recognition
Artificial intelligence
Set (psychology)
business
retrieval
Subjects
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⟩