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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

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

verfasst von : Mansoor Iqbal, Muhammad Awais Rehman, Naveed Iqbal, Zaheer Iqbal

Erschienen in: Intelligent Technologies and Applications

Verlag: 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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Vorheriges Kapitel An Investigation on Ability of Pre-trained Convolutional Neural Networks Trained on ImageNet to Classify Melanoma Images Without Re-training
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Metadaten
Titel
Effect of Laplacian Smoothing Stochastic Gradient Descent with Angular Margin Softmax Loss on Face Recognition
verfasst von
Mansoor Iqbal
Muhammad Awais Rehman
Naveed Iqbal
Zaheer Iqbal
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Intelligent Technologies and Applications

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

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

Copyright-Jahr: 2020

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