Paper Info
Title
Deep Secure Encoding for Face Template Protection
Abstract
In this paper we present a framework for secure identification using deep neural networks, and apply it to the task of template protection for face password authentication. We use deep convolutional neural networks (CNNs) to learn a mapping from face images to maximum entropy binary (MEB) codes. The mapping is robust enough to tackle the problem of exact matching, yielding the same code for new samples of a user as the code assigned during training. These codes are then hashed using any hash function that follows the random oracle model (like SHA-512) to generate protected face templates. The algorithm makes no unrealistic assumptions and offers high template security, cancelability, and matching performance comparable to the state-of-the-art. The efficacy of the approach is shown on CMU-PIE, Extended Yale B, and Multi-PIE face databases. We achieve high (~ 95%) genuine accept rates (GAR) at zero false accept rate (FAR) while maintaining a high level of template security.
Year
DOI
Venue
2016
10.1109/CVPRW.2016.17
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
Field
DocType
deep secure encoding,face template protection,secure identification,face password authentication,deep convolutional neural networks,CNN,face images,maximum entropy binary codes,MEB codes,hash function,genuine accept rates,GAR,false accept rate,FAR
Pattern recognition,Convolutional neural network,Computer science,Random oracle,Theoretical computer science,Artificial intelligence,Hash function,Password authentication protocol,Template,Principle of maximum entropy,Encoding (memory),Binary number
Conference
Volume
Issue
ISSN
2016
1
2160-7508
ISBN
Citations 
PageRank 
978-1-5090-1438-5
2
0.37
References 
Authors
18
4
Name
Order
Citations
PageRank
Rohit Pandey171.83
Yingbo Zhou226319.43
Bhargava Urala Kota3241.73
Venu Govindaraju43521422.00