Face Detection based on Semantic Model for Mobile Banking
Subject Areas : Computer Engineering and IT
leili nosrati
1
(phd student)
Amir Massoud Bidgoli
2
(Department of Computer Engineering, Tehran North Branch,Islamic Azad University, Tehran, Iran)
hamid hajseiedjavadi
3
(shahed university professor)
Keywords: face detection authentication, mobile banking, semantic learning, Genetic Algorithm, Decision tree.,
Abstract :
In this paper, a new authentication protocol for online banking based on the semantic model of features extracted from people's image is introduced. The proposed approach is presented using smart mobile phones for online digital imaging for customers. In this work, a fuzzy clustering has been used to categorize the characteristics of the images of different people and by applying them to different machine learning methods, a combined technique of machine learning classification methods has been presented to improve performance and increases strength against various attacks. Also to reduce the complexity of machine design for operational tasks, the technique of reducing features extracted from face images with the help of genetic algorithm has been used. In the last part, in order to make a decision for authentication selected by machine learning systems, a fuzzy logic system is presented based on the highest accuracy of identifying the desired person. Using a public dataset, the experimental results showed that the genetic algorithm-based technique is the best feature selection to create an implicit authentication method for the smartphone environment. The results showed an accuracy of about 99.80% using only 30 features out of 77 to authenticate users. At the same time, the results showed that the proposed method has a lower error rate compared to the related work.
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