Optimizing Tracking System: The Impact Of Facial Recognition On Security And Efficiency

Abstract

The demand for effective and safe attendance monitoring systems has increased in the modern world, affecting several industries such as public institutions, corporate settings, and education. Traditional methods, such as filling out attendance sections or card-based systems, are error-prone, time-consuming, and vulnerable to fraudulent activity. To solve these problems, this paper introduces an improved convolutional neural network (CNN)-based facial recognition system that increases the efficiency and accuracy of attendance tracking by adjusting the learning rate through adaptive learning and optimizing the features. The proposed system combines off-the-shelf deep architectures with an optimistic lightweight to reduce inference time and enhance their robustness to variable lighting and mask conditions. The optimization explicitly targets both accuracy and speed, improving inference efficiency by tuning learning rates and pruning redundant convolutional layers. The model is optimized experimentally, with the use of the AT&T, LFW and CASIA-WebFace datasets, showing that the optimized model is better than the standard CNN baselines. Such a framework can prevent identity fraud and so-called buddy-punching, as well as enhance data security by encrypting and storing it in privacy-protected storage. But then, the confidentiality problem that arises while using recognized face technology is also very important to focus on. The first step to maintaining the confidentiality of biometric information is to implement clear data protection protocols and to put in place reliable security measures. Lastly, facility biometrics devices could be used to retain attendance reports in place of customary methods that are exhausted by various challenges. The future outbreak of such systems may perform as a necessary tool for presence management process reformation at various industries. The combination of effectiveness, accuracy, and security will lead to effectiveness and operational excellence. The originality of this paper is the hybrid optimization of CNN parameters and introduction of a privacy-conscious design in the modern absence sense.