Paper Info
Title
Face Anti-Spoofing With Multi-Scale Information
Abstract
Face anti-spoofing has encountered increasing demand as one of the key technologies for reliable and safe authentication with faces. Current face anti-spoofing methods generally take a single crop of face region as input for classification, i.e. exploiting information at only one scale. This single-scale scheme mainly focuses on facial characteristics but not utilize the surrounding information, causing poor generalization for different scenarios with varied means of attacks. Besides, it is tedious or highly empirical to determine an optimal scale of face crops. To overcome the limitations of single-scale methods, in this work we propose to integrate Multi-Scale information for better Face ANti-Spoofing (MS-FANS). Specifically, the proposed MS-FANS method takes multiple face crops at different scales as input followed by a convolutional neural network (CNN) for feature extraction. Then the features from different scales form as a sequence, which are fed into a Long Short-Term Memory (LSTM) network for adaptive fusion of multi-scale information, constructing the final representation for classification. Benefited from this multi-scale design, MS-FANS can adaptively utilize context information from multiple scales, leading to promising performance on two challenging face anti-spoofing datasets, Idiap REPLAY-ATTACK and CASIA-FASD, with significant improvement compared with the existing methods.
Year
DOI
Venue
2018
10.1109/ICPR.2018.8546026
2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Field
DocType
ISSN
Authentication,Pattern recognition,Convolutional neural network,Computer science,Feature extraction,Artificial intelligence,Anti spoofing
Conference
1051-4651
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Shiying Luo153.44
Meina Kan271326.32
Shuzhe Wu3572.51
Xilin Chen46291306.27
Shiguang Shan56322283.75