SAS-HRM: Secure Authentication System for Human Resource Management

Abstract

To guarantee data confidentiality and information sensitivity, human resource management requires secure systems. In the field of authorization and dependability in recognizing and identifying persons, facial recognition has grown in importance. In this research, a secure authentication system is proposed based on biometric aspects of the user's face and identifying it using the CNN classification model is provided to give access to human resource management and update data. The system is divided into four major stages: First, set up the system environment, beginning with smart cards, card readers, Arduino, and so on. Second, after undergoing pre-treatment steps, the facial characteristics are extracted using LDA. Third, create a high-accuracy CNN model to recognize and classify the user's face among the system's users. Finally, the user is allowed to enter the system and update his information. When compared to the accuracy of classification using machine learning techniques with a CNN proposed model, the accuracy of the model with LDA was up to 100%. K-NN has 91%, while TD has 94%.