Applying CNN with Extracted Facial Patches using 3 Modalities to Detect 3D Face Spoof

In an era of smart technology where the human face is used for authentication because its fast and convenient but not every technology introduced is 100% credible. Face spoofing has rendered this technology vulnerable because of the damage it can cause when its security is breached. 3D face spoof has emerged and complicated facial recognition systems. Although, sophisticated improvement over the years have been made to reduce the impact of 3D face spoofing, a robust solution that can change the facial recognition sensors from being fooled are still studied and been researched on. In this paper, we proposed a (Convolutional Neural Network, CNN) architecture focused on Fusion-based approach, Depth and Patch-based (Convolutional Neural Network, CNN) by extracting from the human facial images, the facial features and complete detail hints. Our tests were carried out on CASIA-SURF dataset consisting of 3 modalities: Color, Depth and (Infrared, IR).