Design and implementation of access monitoring system for banks using OpenCV and Django

This paper presents a system which configures a web- based access monitoring system for banks. The banking system plays a very significant role in society. There is a need for monitoring the people entering the bank as a part of the procedure for providing security. The traditional monitoring system only records the video, it does not recognize the people entering. This work implements a system for recognizing the people entering the bank using a computer vision technology. With the ongoing development of image processing algorithms, the computer vision discipline of artificial intelligence launching a new era by teaching computers to comprehend and interpret the visual environment. This project proposes an efficient way to keep track of the people entering the banks using face detection. Feature extraction of the images of the employees and the customers is done. The database of the face images of employees of the bank and customers is created and stored in the system. Viola jones algorithm which uses HAAR feature extraction and cascade classifiers is used for the face detection. The face features detected is compared with the existing database and classified as employee, customer and unknown. After the face recognition the information is displayed in a webpage with date and time of entry. This webpage can be very useful in case of Security breach in banks. The face detection algorithm is implemented using OpenCV on a Raspberry pi processor with Linux operating system.