A Robust and Efficient Anti Spoofing
Method for
Facial Recognition Systems using the
Fusion of
Fresnel Transform and Micro-Texture Analysis
Farhood Mousavizadeh1, Keivan Maghooli1, Emad Fatemizadeh2 and Mohammad
Moin3 1Department
of Biomedical Engineering, Islamic Azad University, Iran 2School of Electrical Engineering, Sharif University of
Technology, Iran 3Faculty
of IT, ICT Research Institute (Iran Telecom Research Center), Iran
Abstract: Face biometric systems provide automatic
verification or identification of a person. But nowadays using hacked or stolen
photographs or videos is one of the most common manners for spoofing such
systems. This problem can be solved by using some specific hardware’s like IR
or stereoscopic cameras. However, the additional hardware should be low cost
and applicable for the facial recognition purposes. To deal with the spoofing
problem, we present single image and real-time method that can work with
conventional cameras. Facial images commonly contain surface textures and the
dept characteristics that cannot be found in a photograph and also there are
some differences in the frequency distribution of a real face and a fake one.
These two properties are the basic features of the most liveness detection
systems. In this paper, we aim to utilize an automatically facial liveness
detection method that combines these two features to have a robust and reliable
method for single image liveness detection. We use the fusion of the Zernike
moments of Fresnel transformed images and multi-scale Local Binary Patterns
(LBP) histogram and fed them to Principal Components Analysis (PCA) and
Fisher’s Discriminant Ratio (FDR) analyzers to obtain efficient and rich sets
of features. The results show that we can achieve to the features that are
half/quarter the size of original feature sets using FDR /PCA respectively. The
results show that we could have liveness detection features stronger in
performance and smaller in dimension in comparison with the common and
state-of-the-art methods like LBP.
Keywords: Liveness detection, fresnel transform, local
binary patterns, zernike moments analysis, FDR, PCA.
Received July 28, 2014;
accepted May 11, 2015