NDLI: Weighted similarity and distance metric learning for unconstrained face verification with 3D frontalisation

Content Provider	IET Digital Library
Author	Xu, Xin Liang, Jiuzhen Chen, Chen Hou, Zhenjie
Abstract	In this study, the authors focus on the challenging problem of verifying faces captured under unconstrained conditions. Unconstrained face images often vary largely in poses, illuminations, expressions, occlusions, and ages. To address these challenges, they combine face frontalisation method with metric learning. To deal with the variations of poses, they apply an improved 3D face frontalisation method to generate the frontal view of the face images. Recent studies observed that bilinear similarity and Mahalanobis distance have a promising performance on measuring the similarity of two images. Based on these studies, they propose a weighted similarity and distance metric learning method which balances the role of bilinear similarity and Mahalanobis distance to better measure the similarity of an image pair. All the experiments are conducted based on the labelled faces in the wild database, and the experimental results show the effectiveness of their method.
Starting Page	399
Ending Page	408
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6327
Journal	IET Image Processing
Publisher Date	2018-11-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Authors Focus Bilinear Similarity Computer Vision And Image Processing Technique Distance Metric Learning Method Face Recognition Frontal View Image Pair Image Recognition Labelled Face Learning in AI Mahalanobis Distance Poses Recent Studies Unconstrained Condition Unconstrained Face Image Unconstrained Face Verification Weighted Similarity
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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