There are many different smart home surveillance and control systems, which will need some type of visual identification and classification system. Past models of Deep Learning have had great success in visual identification and image classification particularly in the healthcare and security industries. This study reviews past architecture and applications of Deep Learning and Convolutional Neural Networks. This paper then presents the creation, process, testing, and results of a CNN model with the end objective of identifying images for determination of access rights. Evaluation outcomes show that after 50 forward and backward dataset training passes the deep learning network achieved an identification accuracy of 96.7% and a 98.0% probability of proper classification of access authorization. The results suggest that deep learning models could be successful in strengthening smart home security systems.