FACE LIVENESS DETECTION BASED ON MULTIPLE FEATURE DESCRIPTORS

The face liveness detection module is one of the most important parts in the state-of-the-art face recognition system. In this paper, we present an efficient method to further improve its accuracy by leveraging multiple feature descriptors. Firstly, a data-driven feature descriptor is proposed based on the Karhunen-Loeve Transform (KLT) learned from both client and imposter face images. Moreover, the Completed Local Binary Pattern (CLBP) algorithm is utilized to represent the local structure and the high-middle spectra components of 2D Fourier transform are also utilized to reflect the global structure. These features are fed into the support vector machine (SVM) to learn a classifier for face liveness detection. Experimental results on NUAA illustrate that our proposed method outperforms most of the widely utilized feature descriptors.