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A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofing

Authors : Oeslle Lucena, Amadeu Junior, Vitor Moia, Roberto Souza, Eduardo Valle, Roberto Lotufo

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Face recognition systems are gaining momentum with current developments in computer vision. At the same time, tactics to mislead these systems are getting more complex, and counter-measure approaches are necessary. Following the current progress with convolutional neural networks (CNN) in classification tasks, we present an approach based on transfer learning using a pre-trained CNN model using only static features to recognize photo, video or mask attacks. We tested our approach on the REPLAY-ATTACK and 3DMAD public databases. On the REPLAY-ATTACK database our accuracy was 99.04% and the half total error rate (HTER) of 1.20%. For the 3DMAD, our accuracy was of 100.00% and HTER 0.00%. Our results are comparable to the state-of-the-art.

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Metadata
Title
Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofing
Authors
Oeslle Lucena
Amadeu Junior
Vitor Moia
Roberto Souza
Eduardo Valle
Roberto Lotufo
Copyright Year
2017
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-59875-8

Electronic ISBN: 978-3-319-59876-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59876-5_4

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