Campus innovation can ensure significant advancements in the computer vision industry by implementing a smart attendance system (SAS). The Internet of Things (IoT) is being utilized in conjunction with the deep learning technique for facial identification, using convolutional neural networks (CNNs), to automatically detect faces and track attendance with high accuracy and precision. In order to develop a real-time program that deals with the rote activities of controlling the attendance system in a facility, this research work has focused on detecting a single image. The procedure entails identifying faces using security camera footage captured at various points across the campus, as well as from other system-related information technologies. Using the proposed hierarchal multi-task cascaded convolutional networks (MTCNNs) on small datasets and, in particular, deep learning recognition functions, the experimental results demonstrate that the Yale Database is one of the best datasets for tackling practice tasks in face recognition.