NDLI: Automated cleaning of identity label noise in a large face dataset with quality control

Content Provider	IET Digital Library
Author	Jazaery, Mohamad Al Guo, Guodong
Abstract	For face recognition, some very large-scale datasets are publicly available in recent years, which are usually collected from the Internet using search engines, and thus have many faces with wrong identity (ID) labels (outliers). Additionally, the face images in these datasets have different qualities because of uncontrolled situations. The authors propose a novel approach for cleaning the ID label error, handling face images in different qualities. The face ID labels cleaned by their method can train better models for low-quality face recognition since more low-quality images are correctly labelled for training a deep model. In their low-to-high-quality face verification experiments, the deep model trained on their cleaning results of MS-Celeb-1M.v1 face dataset outperforms the same model trained on the same dataset cleaned by the semantic bootstrapping method. They also apply their ID label cleaning method on a subset of the cross-age celebrity dataset (CACD) face dataset, in which their quality-based cleaning can deliver higher precision and recall than a previous method on detecting the ID label errors.
Starting Page	25
Ending Page	30
Page Count	6
ISSN	20474938
Volume Number	9
e-ISSN	20474946
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/9/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2019.0081
Journal	IET Biometrics
Publisher Date	2019-09-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Cleaning CACD Face Dataset Cleaning Result Computer Vision And Image Processing Technique Deep Model Face ID Labels Face Image Face Recognition ID Label Cleaning Method ID Label Error ID Label Noise Image Recognition Information Network Internet Knowledge Engineering Technique Large-scale Datasets Learning in AI Low-quality Face Recognition Low-quality Image Low-to-high-quality Face Verification Experiments Quality Control Quality-based Cleaning Search Engines Size 1.0 M Wrong Identity Labels
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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