Considering that all moving objects are humans, much of the work in data is based on recognizing and tracking moving objects. In this work, we present a method for counting people's faces. Even though we use the face mask, the deep learning-based YOLOv5 algorithm and Faster R-CNN allow us to recognize the face. We do a very good job of counting people. To make the calculation more accurate, we introduced a new type of intelligent small scale computing system consisting of cheaper hardware and user-friendly cloud computing software. These findings show that intelligent computing systems can realize human vision. Additionally, by combining inexpensive hardware with cloud-based software, the planning process becomes more transparent and cost-effective. Finally, the web application allows users to view the number of authorized and unauthorized users. Based on the results obtained from this method, the deep learning YOLOv5 algorithm is used to identify and match human images to increase security, and thanks to cloud storage, users can easily view all calculated results, increasing the accuracy by 98.53%. Owing to the truth that most of the secure watches cannot be able to check each and each individual physically, prevalent cameras regularly utilize sensors to assist in identifying and monitoring individuals from a distance.