Abstract: The primary goal of this project is to develop a Passive Facial Liveness Detection (attendance monitoring system) that will improve and update the current attendance system, making it more efficient and effective. The existing system is rife with flaws, making attendance tracking incorrect and inefficient. When the authority is unable to enforce the previous system's regulations, a slew of problems occur. Software that recognises faces will be used. The face is one of the natural characteristics that can be used to identify someone. As a result, it's commonly utilised to track down someone's identify because the chances of a face deviating or being cloned are slim. This project will develop face databases to feed information into the recognizer algorithm. Faces will be linked to the database during the attendance-taking session to determine identity. When a person is recognised, their attendance is automatically recorded and the essential data is saved in an excel file. An excel document including all participants' attendance information is emailed to the appropriate academics at the end of the day.

Keywords: Python , NumPy, OpenCV, Hadoop, Pandas, facial liveness.

| DOI: 10.17148/IARJSET.2022.94104