The information system for the liveness detection process using aws

Abstract

Analysis of recent studies and publications. The analysis of the modern market of software and algorithmic solutions for performing the Liveness detection process showed that the currently existing approaches are completely commercial solutions with closed algorithms of their work. In addition, the Liveness detection algorithm is not yet standardized, and has many implementation options that can sometimes lead to am-biguous results [7]. That is why, it was decided to develop our own algorithm and liveness detection system based on obtaining face characteristics using the AWS API [8], because this service offers a high accuracy of face recognition, which is 99.99%, and provides 10,000 free requests to use every month, which enough to fulfill the purpose of our work. Purpose of the study. Development of the algorithm for the Liveness Detection process using AWS and the construction of a video identification system based on it. Main research material. This paper investigates the video identification of a per-son using the Liveness Detection process. The basis of the question, complexity of the Liveness Detection process and the implementation of biometric human video identifica-tion have been studied. An algorithm for conducting the Liveness Detection process based on the execution of random tasks by an identified person has been developed. Integration with the Amazon DetectFaces API was carried out in order to obtain the character of a digital image of the head, as a result of which it becomes possible to analyze a photo of a person for a wide range of key facial features. A manual test of the Amazon DetectFaces API was conducted, in the process of which, empirically, thresh-olds of facial characteristics in the image for the Liveness Detection process were set. Integration with the Amazon CompareFaces API was implemented to execute the bio-metric video identification. The testing of the developed Liveness Detection application system based on the created Liveness passing algorithm and selected AWS API thresholds consisted of 100 tests of different orientations, from direct user-flow testing to tests with a medical mask, glasses and beard, a photo of a face and a video image of a person for the purpose of forgery his personality. Thanks to the initial accuracy of Amazon API facial recognition of 99.99% and the random selection of tasks in the developed Liveness Detection algo-rithm, all tests gave the expected result - the person was either successfully video-identified at the enterprise, or his identity was faked during the Liveness Detection pro-cess. Conclusions. Based on the research, the software system of biometric human video identification has been developed, which allows to automatically establish the presence of a person in front of a video camera and identify it, preventing the use of fake artifacts to falsify a person's identity.