Paper Info
Title
Face Spoofing Attack Detection Using Spatial Frequency and Gradient-Based Descriptor.
Abstract
Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.
Year
DOI
Venue
2019
10.3837/tiis.2019.02.022
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Keywords
Field
DocType
Video-based face spoofing,Face recognition,Fourier transform,SIFT,spatial frequency
Computer vision,Scale-invariant feature transform,Facial recognition system,Spoofing attack,Computer science,Fourier transform,Artificial intelligence,Spatial frequency,Distributed computing
Journal
Volume
Issue
ISSN
13
2
1976-7277
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Zahid Ali101.01
Unsang Park281536.32