Generation of an EDS Key Based on a Graphic Image of a Subject’s Face Using the RC4 Algorithm

Abstract

Modern facial recognition algorithms make it possible to identify system users by their appearance with a high level of accuracy. In such cases, an image of the user’s face is converted to parameters that later are used in a recognition process. On the other hand, the obtained parameters can be used as data for pseudo-random number generators. However, the closeness of the sequence generated by such a generator to a truly random one is questionable. This paper proposes a system which is able to authenticate users by their face, and generate pseudo-random values based on the facial image that will later serve to generate an encryption key. The generator of a random value was tested with the NIST Statistical Test Suite. The subsystem of image recognition was also tested under various conditions of taking the image. The test results of the random value generator show a satisfactory level of randomness, i.e., an average of 0.47 random generation (NIST test), with 95% accuracy of the system as a whole.

MNCF: Prediction Method for Reliable Blockchain Services under a BaaS Environment

“…They have provided acceptable accuracy with a shorter computational time. Recently, machine learning and deep learning have progressed rapidly and resulted in significant achievements in several fields, such as natural language processing [16], computer vision [17], and recommendation systems [18]. Since deep neural networks can effectively learn complex data representation, studies regarding neural networks can be applied to construct complex relationships among users and services.…”