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Robust Deep Face Recognition with Label Noise

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Details

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
PublisherSpringer Verlag
Pages593-602
Number of pages10
ISBN (Print)9783319700953
DOIs
Publication statusPublished - 2017
Publication typeA4 Article in a conference publication
EventINTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING -
Duration: 1 Jan 1900 → …

Publication series

NameLecture Notes in Computer Science
Volume10635
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceINTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING
Period1/01/00 → …

Abstract

In the last few years, rapid development of deep learning method has boosted the performance of face recognition systems. However, face recognition still suffers from a diverse variation of face images, especially for the problem of face identification. The high expense of labelling data makes it hard to get massive face data with accurate identification information. In real-world applications, the collected data are mixed with severe label noise, which significantly degrades the generalization ability of deep learning models. In this paper, to alleviate the impact of the label noise, we propose a robust deep face recognition (RDFR) method by automatic outlier removal. The noisy faces are automatically recognized and removed, which can boost the performance of the learned deep models. Experiments on large-scale face datasets LFW, CCFD, and COX show that RDFR can effectively remove the label noise and improve the face recognition performance.

Keywords

  • Deep learning, Face recognition, Noise removal

Publication forum classification

Field of science, Statistics Finland