Development of a Computer System for Identity Authentication Using Artificial Neural Networks

Abstract

The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics' changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

Intelligent Authentication for Identity and Access Management: A Review Paper

“…While [20] proposed the computation of an authentication score based on a user's recent activity. [21] Utilize automated face recognition to authenticate identity. In [22] a multimodal digital identity management system was proposed as a solution for managing digital identity information in an effort to reduce the cases of identity fraud and theft, an artificial intelligence technology is used to implement a technique of information fusion to combine the user's credential attributes for optimum recognition.…”

Intelligent Authentication for Identity and Access Management: A Review Paper

“…Table1 displays the comparison result and show the ideal authentication framework for IAM will face all challenges. [27] No No No Yes [22] Yes No No Yes [23] Yes Yes Yes No [11] Yes No No Yes [20] No No Yes No [21] Yes Yes No Yes [26] Yes No No Yes [28] No…”

Image Analysis & Stereology: 2017 Research Highlights

“…As a computer vision application, Kartbaev et al (2017) proposed an approach to the implementation of an identity authentication system based on image face recognition that, besides comparing the image with reference samples, takes into account also the change of face features over time (e.g., due to aging). For the purpose of increasing the efficiency of automated face recognition, the proposed approach is based on the adaptive neuro-fuzzy inference system (ANFIS), a special type of a fuzzy logic inference that is combined with an artificial neural network (AAN) architecture for supervised learning that reduces the rate of errors in the determination of rules in fuzzy logic.…”