Currently, Face Recognition is the most used biometric to determine an individual’s identity due to its natural and unobtrusive nature. This study proposes face recognition and verification of two-dimensional(2D) images by a feature extraction algorithm which involves identifying anthropological facial feature points and calculating the Euclidean Distance (ED) between these points (ED-FFP) as these distances, if required can be used as an objective measure during trials in courts. These measurements are then used as inputs for various classification methods, including Logistic Regression Classifier (LR), Decision Tree (DT), Naive Bayes (NB), and two other classifiers using the Ensemble Learning Model. The method was tested on 2D face image databases (Caltech, Yale, and ORL) and found to be more efficient and accurate for face recognition than other methods, with a maximum accuracy of 85% for predicting distinct faces using the Decision Tree classifier model. The ensemble learning model also had an accuracy of 85%, which could potentially be improved by using more photos for comparison. In future work, the method could be applied to 3D images, which is currently an open challenge in the field.