A Non-intrusive and Cover Resistant Method for Detecting Forgery in Face Recognition Using Deep Learning

In the use of face recognition systems, various fraud, such as the use of a mask and a photo of a genuine person, are two major problems that limit their applications. Studies have shown a number of methods for detecting fraud in face recognition, which are sometimes intrusive, enforcing the person to make a move in order to distinguish the real face from the fake one. The use of intrusive methods often leads to user dissatisfaction. In this article, we present a non-intrusive method using features such as light reflection or the presence of periodic noise to distinguish real images from the fake one. In this method, the edges and texture of the image are highlighted by a local binary pattern to better detect fraud. Then, by extracting the image feature using a deep learning technique with three layers of convolution, it will be able to distinguish between real and fake face images. This method is resistant to covering the eyes and face. In order to evaluate the proposed method, the CASIA dataset was used in this research. The results show 98% accuracy of the proposed method on this dataset. Among the existing methods, we see an increase in accuracy.