NDLI: Combined training strategy for low-resolution face recognition with limited application-specific data

Content Provider	IET Digital Library
Author	Zeng, Dan Spreeuwers, Luuk Veldhuis, Raymond Zhao, Qijun
Abstract	Application-specific data for certain biometric applications are often not sufficiently available. The authors present a solution for face recognition with limited application-specific data. Existing methods often use a classifier with convolutional neural networks (CNNs) as feature extractors. The CNNs are trained with massive general (i.e. not application specific) data and the classifier is trained with application-specific data. Alternatively, the authors propose a combined training strategy to train the classifier on a balanced mixture of general and application-specific data, such that the recognition performance is maximised. The proposed method largely alleviates the needs for application-specific data. To prove its effectiveness, they apply the proposed method to low-resolution face recognition. Specifically, they use the heterogeneous joint Bayesian (HJB) classifier that is capable of comparing features from the same modality but with different characteristics. To further boost performance, the authors augment the training data by pre-processing it to resemble application-specific data. They conducted extensive experiments on challenging datasets, namely, SCface and COX. The results show that the proposed method improves the true match rate on SCface at a false match rate of 10% by ∼11% and the true match rate on COX at a false match rate of 1% by ∼12%.
Starting Page	1790
Ending Page	1796
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 10, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5732
Journal	IET Image Processing
Publisher Date	2019-06-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Biomimetics CNN Classifier Architecture Combined Training Strategy Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network COX Face Recognition Feature Extraction Feature Extractors Heterogeneous Joint Bayesian Classifier HJB Classifier Image Recognition Image Resolution Learning in AI Limited Application-specific Data Low-resolution Face Recognition Neural Computing Technique Neural Net Architecture SCface Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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