  In recent years, face biometric security systems are rapidly increasing,
therefore, the presentation attack detection (PAD) has received significant
attention from research communities and has become a major field of research.
Researchers have tackled the problem with various methods, from exploiting
conventional texture feature extraction such as LBP, BSIF, and LPQ to using
deep neural networks with different architectures. Despite the results each of
these techniques has achieved for a certain attack scenario or dataset, most of
them still failed to generalized the problem for unseen conditions, as the
efficiency of each is limited to certain type of presentation attacks and
instruments (PAI). In this paper, instead of completely extracting hand-crafted
texture features or relying only on deep neural networks, we address the
problem via fusing both wide and deep features in a unified neural
architecture. The main idea is to take advantage of the strength of both
methods to derive well-generalized solution for the problem. We also evaluated
the effectiveness of our method by comparing the results with each of the
mentioned techniques separately. The procedure is done on different spoofing
datasets such as ROSE-Youtu, SiW and NUAA Imposter datasets.
  In particular, we simultanously learn a low dimensional latent space
empowered with data-driven features learnt via Convolutional Neural Network
designes for spoofing detection task (i.e., deep channel) as well as leverages
spoofing detection feature already popular for spoofing in frequency and
temporal dimensions ( i.e., via wide channel).
