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The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning Read chapter Read first chapter

2017 | OriginalPaper | Chapter

2. Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest

Authors : Yury Vizilter, Vladimir Gorbatsevich, Andrey Vorotnikov, Nikita Kostromov

Published in: Deep Learning for Biometrics

Publisher: Springer International Publishing

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Abstract

The family of real-time face representations is obtained via Convolutional Network with Hashing Forest (CNHF). We learn the CNN, then transform CNN to the multiple convolution architecture and finally learn the output hashing transform via new Boosted Hashing Forest (BHF) technique. This BHF generalizes the Boosted Similarity Sensitive Coding (SSC) approach for hashing learning with joint optimization of face verification and identification. CNHF is trained on CASIA-WebFace dataset and evaluated on LFW dataset. We code the output of single CNN with 97% on LFW. For Hamming embedding we get CBHF-200 bit (25 byte) code with 96.3% and 2,000-bit code with 98.14% on LFW. CNHF with 2,000\(\times \)7-bit hashing trees achieves 93% rank-1 on LFW relative to basic CNN 89.9% rank-1. CNHF generates templates at the rate of 40\(+\) fps with CPU Core i7 and 120\(+\) fps with GPU GeForce GTX 650.

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previous chapter The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning
next chapter CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection
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Metadata
Title
Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest
Authors
Yury Vizilter
Vladimir Gorbatsevich
Andrey Vorotnikov
Nikita Kostromov
Copyright Year
2017
Book
Deep Learning for Biometrics

Print ISBN: 978-3-319-61656-8

Electronic ISBN: 978-3-319-61657-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-61657-5_2

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