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Projective Representation Learning for Discriminative Face Recognition Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Improved Face Verification with Simple Weighted Feature Combination

Authors : Xinyu Zhang, Jiang Zhu, Mingyu You

Published in: Computer Vision

Publisher: Springer Singapore

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Abstract

Since the appearance of deep learning, face verification (FV) has made great progress with large scale datasets, well-designed networks, new loss functions, fusion of models and metric learning methods. However, incorporating all these methods obviously takes a lot of time both at training and testing stages. In this paper, we just select training images randomly without any clean and alignment procedure. Then we propose a simple weighted average method which combines features of the last two layers with different weights on the modified VGGNet, named as CB-VGG. It is significantly reducing the complexity of time that one model can be treated as two models. LMNN is used as a post-processing procedure to improve the discrimination of the combined features. Our experiments show relatively competitive results on LFW, CFP, and CACD datasets.

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previous chapter Projective Representation Learning for Discriminative Face Recognition
next chapter Pore-Scale Facial Features Matching Under 3D Morphable Model Constraint
Footnotes
1
Because of lacking of data, we couldn’t report the work of Sankarana et al. [26].
 
2
Due to the restrictions of memory and time, we don’t conduct an experiment on 100 K dataset with multiple crop. The number of images is less than original images which is due to failing detection.
 
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Metadata
Title
Improved Face Verification with Simple Weighted Feature Combination
Authors
Xinyu Zhang
Jiang Zhu
Mingyu You
Copyright Year
2017
Publisher
Springer Singapore
Book
Computer Vision

Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7302-1_2

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