| dc.description.abstract | In 2020, Coivd-19 has swept the world and let lots of countries be on lockdown. Therefore, people have to stay at home and rely on 3C products to complete their daily purchases. However, since relying heavily on the Internet, the security of the dissemination of important information is facing challenges. Biometric identification has become the latest security protection trend. Biometric recognition is based on biological or behavioral characteristics (such as: face, iris, voiceprint, fingerprint, signature, etc.) to recognize identity. Face recognition is unique, and mobile phones can capture faces, so the cost can be low. Furthermore, the face does not need to be in direct contact when capturing images. This is a great advantage in this precarious time. However, there are still many ways to make the facial recognition system fail, such as print attack or replay attack.
A common face recognition attack — Presentation Attacks (PA) [1], whose main purpose is to impersonate others or avoid being recognized by the system. It mainly includes print attack (such as photos, printed pictures), replay attack (playing video), 3D mask attack; judging the difficulty of a spoof face attack, from easy to difficult: photo attack, video replay attack, print attack, 3D mask attack. Thus, print attack and replay attack are the most common attacks.
We compared the different structures of CNN by using photos and printed pictures as the spoof face dataset, and finally found that although the CNN model is the simplest neural network, it beats most complex models. Our research shows that a CNN model with 5 to 8 layers can achieve 99% accuracy and recall, thus 6 and 7 layers CNN model can get the best performance. | en_US |