A robust and efficient anti spoofing method for facial recognition systems using the fusion of Fresnel transform and micro-texture analysis

Abstract EN

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.