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2020 | OriginalPaper | Chapter

Effect of Laplacian Smoothing Stochastic Gradient Descent with Angular Margin Softmax Loss on Face Recognition

Authors : Mansoor Iqbal, Muhammad Awais Rehman, Naveed Iqbal, Zaheer Iqbal

Published in: Intelligent Technologies and Applications

Publisher: Springer Singapore

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Abstract

An important task in deep learning for face recognition is to use proper loss functions and optimization technique. Several loss functions have been proposed using stochastic gradient descent for this task. The main purpose of this work is to propose the strategy to use the Laplacian smoothing stochastic gradient descent with combination of multiplicative angular margin to enhance the performance of angularly discriminative features of angular margin softmax loss for face recognition. The model is trained on a most popular face recognition dataset CASIA-WebFace and it achieves the state-of-the-art performance on several academic benchmark datasets such as Labeled Face in the Wild (LFW), YouTube Faces (YTF), VGGFace1 and VGGFace2. Our method achieves a new record accuracy of 99.54% on LFW dataset. On YTF dataset it achieves 95.53% accuracy.

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Metadata
Title
Effect of Laplacian Smoothing Stochastic Gradient Descent with Angular Margin Softmax Loss on Face Recognition
Authors
Mansoor Iqbal
Muhammad Awais Rehman
Naveed Iqbal
Zaheer Iqbal
Copyright Year
2020
Publisher
Springer Singapore
Book
Intelligent Technologies and Applications

Print ISBN: 978-981-15-5231-1

Electronic ISBN: 978-981-15-5232-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-5232-8_47

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