NDLI: Human-level face verification with intra-personal factor analysis and deep face representation

Content Provider	IET Digital Library
Author	Munasinghe, Sarasi Fookes, Clinton Sridharan, Sridha
Abstract	The last two decades have seen an escalating interest in methods for large-scale unconstrained face recognition. While the promise of computer vision systems to efficiently and accurately verify and identify faces in naturally occurring circumstances still remains elusive, recent advances in deep learning are taking us closer to human-level recognition. In this study, the authors propose a new paradigm which employs deep features in a feature extractor and intra-personal factor analysis as a recogniser. The proposed new strategy represents the face changes of a person using identity specific components and the intra-personal variation through reinterpretation of a Bayesian generative factor analysis model. The authors employ the expectation-maximisation algorithm to calculate model parameters which cannot be observed directly. Recognition outcomes achieved through benchmarking on large-scale wild databases, Labeled Faces in the Wild (LFW) and Youtube Face (YTF), clearly prove that the proposed approach provides remarkable face verification performance improvement over state-of-the-art approaches.
Starting Page	467
Ending Page	473
Page Count	7
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 5, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0050
Journal	IET Biometrics
Publisher Date	2018-02-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Computer Vision Computer Vision And Image Processing Technique Computer Vision System Deep Face Representation Deep Features Deep Learning Expectation-maximisation Algorithm Face Recognition Feature Extraction Feature Extractor Human-level Face Verification Human-level Recognition Image Recognition Image Representation Intra-personal Variation Intrapersonal Factor Analysis Labeled Face Recognition Large-scale Unconstrained Face Recognition Learning in AI Recognition Outcomes Remarkable Face Verification Performance Improvement Statistics YouTube Face Recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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