A Community Detection Approach to Cleaning Extremely Large Face Database

المؤلفون المشاركون

Dou, Yong
Jin, Chi
Jin, Ruochun
Chen, Kai

المصدر

Computational Intelligence and Neuroscience

العدد

المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-10، 10ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2018-04-22

دولة النشر

مصر

عدد الصفحات

10

التخصصات الرئيسية

الأحياء

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1130722