Description

The efficiency of biometric systems, in particular efficient and accurate biometric identification, is one of the most challenging open problems in biometrics today. As a solution, this work proposes an efficient privacy-preserving reduction of the computational workload of biometric identification systems using public-key encryption with keyword search. The system is identified the person is authorized or un-authorized. If the person is authorized, the user can view the original information or else the user can’t able to view the information. The system is developed the machine learning algorithm for classifying the person is authorized or un-authorized by using random forest classifier. Finally, the experimental results shows that some performance metrics such as accuracy and error rate.