Face Spoofing Detection Using Deep CNN

Abstract

3D mask face spoofing attack is an important challenge in recent years and draws further study. Because of the number of deficiencies and small differences in the database, however, a few methods can be proposed to aim at it meanwhile, most developed databases are focused on countering various types of threats and neglect environmental developments in implementations in the real world. The 3D mask against spoofing is used in this paper to simulate the real-world scenario with other options. The database used in the proposed method includes 10 different subject masks (7 subject 3D latex masks and 2 subjects for 2D paper masks and 1 for half mask from below the eye is using to testing the result). Therefore, the total size is 440 videos 400 is fake videos, and 40 is a real video.  The directions for future study are shown in the benchmarking experiments. We intend to release the database platform to evaluate various methods this system has been used for a deep Convolution neural network. The result is robust for an eye-blink recognition technique. There are three basic steps in the proposed system: Firstly, video pre-processing, facial recognition, and finally, the output step whether the video is true or falsified. The method used is stronger than most techniques. The suggested approach in this study was used for the MLFP dataset and was very reliable and accurate as a result of the whole experiment and the accuracy obtained is (99.88).