Classification algorithms have been found to produce a better result for monitoring e-examination in terms of performance and accuracy in detecting examination impersonation in an e-learning environment. This paper presents the results of the comparative analysis of six classification algorithms (Logistic Regression, Multi-Layer Perceptron, Support Vector Machine, Random Forest, Bayes Network and Stochastic Gradient Descent) for face matching and verification in e-examination. This were compared and evaluated based on True Positive Rate (TPR), False Positive Rate (FPR), Precision, Recall, F-measure, Kappa Statistics (KS), Accuracy, Time to build model and Mean Absolute Error, using Waikato Environment for Knowledge Analysis (WEKA) to determine the best fit classifiers for the design of the model. The developed model was tested, using 250 facial images (dataset) acquired from the entire National Diploma students of Computer Science, Yaba College of Technology. The best fit classifiers (LR-based on Logistic Loss Function and SGD-Stochastic Gradient Descent) obtained from the comparison was used for binary classification, image optimization and monitoring. The results from the comparison showed that LR and SGD had leading performances with TPR, FPR, Precision, Recall, F- Measure, KS and Accuracy values of 100%, Mean Absolute Error value of zero. LR had shortest time of 0.01 second and SGD with 0.04 second, based on time taken to build model. LLF of LR and SGD played significance role in producing faster and optimal face recognition results. The technique can be employed by examiners and learning management specialists to conduct malpractice free e-examination.

Keywords : Classification Algorithms; E-Examination; Face Matching; Face Verification; E-Learning Environment.