MACHINE LEARNING ALGORITHMS IN FACIAL IDENTITY VERIFICATION FOR COMPUTER-BASED ASSESSMENTS

This review paper analyses the utilization of machine learning algorithm in facial identity verification of computer-based assessments. Through the use of facial recognition, this project aims to increase security by letting a customer know who is actually using the system. Only authorized customers are able to enter the framework. Neural learning approaches for face recognition can be used for feature extraction and training modules. Furthermore, a lot of people use similar methods to extract information from photographs of people. Certain detection systems can perform full body scans, as well as iris and finger print recognition. The purpose of these systems’ deployment is safety and security. In this study, we examine many facial recognitions machine learning techniques. Four supervised machine-learning classifiers for face recognition are taken into consideration: Support Vector Machine (SVM), 1-nearest neighbor (1-NN), Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). The accuracy with which different categorization schemes can recognize a face is another way in which their efficacy is proven and evaluated. The process of identifying faces of individuals whose photos are kept in databases and made available as datasets is known as face recognition. Numerous tests carried out using these datasets. Which machine learning method is the best in terms of picture detection accuracy is made evident by the comparative study. Even if there are other very successful identification techniques, face recognition has remained a prominent area of study interest because it is a simple and non-intrusive way for people to identify themselves. The results of this study could be helpful in determining the best machine-learning method to improve the accuracy of facial recognition.