Presentation Attack Detection using Convolutional Neural Networks and
  Local Binary Patterns
Justin Spencer Deborah Lawrence Prosenjit Chatterjee Kaushik Roy Albert Esterline Jung-Hee Kim
The use of biometrics to authenticate users and control access to secure
areas has become extremely popular in recent years, and biometric access
control systems are frequently used by both governments and private
corporations. However, these systems may represent risks to security when
deployed without considering the possibility of biometric presentation attacks
(also known as spoofing). Presentation attacks are a serious threat because
they do not require significant time, expense, or skill to carry out while
remaining effective against many biometric systems in use today. This research
compares three different software-based methods for facial and iris
presentation attack detection in images. The first method uses Inception-v3, a
pre-trained deep Convolutional Neural Network (CNN) made by Google for the
ImageNet challenge, which is retrained for this problem. The second uses a
shallow CNN based on a modified Spoofnet architecture, which is trained
normally. The third is a texture-based method using Local Binary Patterns
(LBP). The datasets used are the ATVS-FIr dataset, which contains real and fake
iris images, and the CASIA Face Anti-Spoofing Dataset, which contains real
images as well as warped photos, cut photos, and video replay presentation
attacks. We also present a third set of results, based on cropped versions of
the CASIA images.