A Community Detection Approach to Cleaning Extremely Large Face Database
Joint Authors
Dou, Yong
Jin, Chi
Jin, Ruochun
Chen, Kai
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-22
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Though it has been easier to build large face datasets by collecting images from the Internet in this Big Data era, the time-consuming manual annotation process prevents researchers from constructing larger ones, which makes the automatic cleaning of noisy labels highly desirable.
However, identifying mislabeled faces by machine is quite challenging because the diversity of a person’s face images that are captured wildly at all ages is extraordinarily rich.
In view of this, we propose a graph-based cleaning method that mainly employs the community detection algorithm and deep CNN models to delete mislabeled images.
As the diversity of faces is preserved in multiple large communities, our cleaning results have both high cleanness and rich data diversity.
With our method, we clean the extremely large MS-Celeb-1M face dataset (approximately 10 million images with noisy labels) and obtain a clean version of it called C-MS-Celeb (6,464,018 images of 94,682 celebrities).
By training a single-net model using our C-MS-Celeb dataset, without fine-tuning, we achieve 99.67% at Equal Error Rate on the LFW face recognition benchmark, which is comparable to other state-of-the-art results.
This demonstrates the data cleaning positive effects on the model training.
To the best of our knowledge, our C-MS-Celeb is the largest clean face dataset that is publicly available so far, which will benefit face recognition researchers.
American Psychological Association (APA)
Jin, Chi& Jin, Ruochun& Chen, Kai& Dou, Yong. 2018. A Community Detection Approach to Cleaning Extremely Large Face Database. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130722
Modern Language Association (MLA)
Jin, Chi…[et al.]. A Community Detection Approach to Cleaning Extremely Large Face Database. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1130722
American Medical Association (AMA)
Jin, Chi& Jin, Ruochun& Chen, Kai& Dou, Yong. A Community Detection Approach to Cleaning Extremely Large Face Database. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130722
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130722