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Effective and Generalizable Graph-Based Clustering for Faces in the Wild
Joint Authors
Chang, Leonardo
Pérez-Suárez, Airel
González-Mendoza, Miguel
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-14
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Face clustering is the task of grouping unlabeled face images according to individual identities.
Several applications require this type of clustering, for instance, social media, law enforcement, and surveillance applications.
In this paper, we propose an effective graph-based method for clustering faces in the wild.
The proposed algorithm does not require prior knowledge of the data.
This fact increases the pertinence of the proposed method near to market solutions.
The experiments conducted on four well-known datasets showed that our proposal achieves state-of-the-art results, regarding the clustering performance, also showing stability for different values of the input parameter.
Moreover, in these experiments, it is shown that our proposal discovers a number of identities closer to the real number existing in the data.
American Psychological Association (APA)
Chang, Leonardo& Pérez-Suárez, Airel& González-Mendoza, Miguel. 2019. Effective and Generalizable Graph-Based Clustering for Faces in the Wild. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129519
Modern Language Association (MLA)
Chang, Leonardo…[et al.]. Effective and Generalizable Graph-Based Clustering for Faces in the Wild. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1129519
American Medical Association (AMA)
Chang, Leonardo& Pérez-Suárez, Airel& González-Mendoza, Miguel. Effective and Generalizable Graph-Based Clustering for Faces in the Wild. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129519
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129519