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BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions
[article]
2020
arXiv
pre-print
Preserved Fulltext
We present BioMetricNet: a novel framework for deep unconstrained face verification which learns a regularized metric to compare facial features. ...
In this paper we present this general framework, first of its kind for facial verification, and tailor it to Gaussian distributions. ...
Conclusions We have presented a novel and innovative approach for unconstrained face verification mapping learned discriminative facial features onto a regularized metric space, in which matching and non-matching ...
arXiv:2008.06021v1
fatcat:5kzh2edrinf5joetjzfdiyucoy
A Survey of Face Recognition
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. ...
This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets ...
Conclusion In this paper, we introduce about 100 algorithms in face recognition (FR), including every sides of FR, such as its history, pipeline, algorithms, training and evaluation datasets and related ...
arXiv:2212.13038v1
fatcat:w62ubyfknbht7jl6dzkogp4sne