Multifarious Face Attendance System using Machine Learning and Deep Learning

The process for recording attendance depends on a variety of variables including the quantity of participants, locations, events and time intervals where attendance must be collected. Recording and collecting attendance for an event or program is very much essential to keep track of the intended audience. For example, in schools and colleges roll call can be used to take attendance for the presenteeism in the classroom. Similarly, you can use this process for programs or events with a smaller number of participants. If participants have more attendance maintaining system is tedious process, if it is done manually. So, there is a need for an effective attendance system so that it can monitor the presents in all the ways. So here a multifarious face attendance system is implemented which involves machine learning and deep learning for face detection using MT-CNN and VGGFace2 large scale face recognition dataset model. The execution of this attendance system can be demonstrated for the educational institution. So, the problem of fraudulent attendance and proxies can be resolved by utilizing this technique. The main task of this system is to recognize multiple faces using VGGFace2 and map attendance based on it. So that thereby allowing us to analyze the attendees of the program or function. Following this, the discovered faces can be compared by cross-referencing with the student face database. The attendance and records of attendees can be effectively maintained with the help of this effective technique.