An essential part of the Guardmypi project is facial recognition allowing the system to distinguish between residents and intruders. The first method of facial recognition being tested utilises the HAAR Cascade, a machine learning-based approach where we have used many positive and negative photos to train the classifier. Running a more relaxed training model (see left, run time: 30mins) will allow the classifier to make more false positives during the training stages and allow for quicker model testing. Conversely, we have a stricter classifier training stage (see right run time 72+ hours) that will ideally train the model to have very few false alarms and ideally implemented in our final solution.