RFID, Fingerprint identification, and Iris identification techniques are used to register attendance in industries and organizations. Among all these personal identification strategies, face recognition consumes less time and is highly efficient. It has several applications in attendance management systems and security systems. In this work, a system is implemented that takes attendance for students and staff during their entry into classes or campus, employees in industries, etc, using RFID and face recognition technology. A time period can be set for taking attendance using the dashboard application which is developed using android and the database is automatically uploaded into the webserver through internet connectivity. This process is done without any human intervention. In this system, a Raspberry Pi is installed with OpenCV library and a Camera module is connected for facial Detection and Recognition. Also, an RFID reader is connected to read and verify the person's identity. The data is stored in the Firebase Database and can be accessed through Python programming. The attendance gets updated in the database and can be viewed through the developed Android application.

Traditional Street cleaning procedures demand larger manpower. It necessitates the identification of people who litter on the roadside using scientific approaches. In the proposed system, a deep neural network algorithm named Convolutional Neural Network (CNN), precisely Mask Region Convolutional Neural Network (MRCNN) is used a l o n g with t h e c a m e r as fixed a t t h e crowded areas such as bus stops and markets. This deep learning method is used in the suggested schema to evaluate the photos of streets and detect litter (if any) in them. The detection of litter further progresses with the identification of the person who has thrown it. This technique uses the built-in library files of Python, to generate and compare face encodings. Moreover, it is involved in assisting with better monitoring and reducing operational costs.