Andy Jiang¹ and Yu Sun², ¹USA, ²California State Polytechnic University, USA

Since the beginning of the COVID-19 pandemic, education largely shifted away from the physical classroom and towards more digitally oriented platforms. This simplified classroom attendance problems greatly, as newly created programming scripts could easily track the students in a meeting room via their names. However, with the recent growing return to in person education, it has become apparent that the problem of attendance within the context of a non-virtual classroom environment has yet to be solved in an efficacious automated fashion. In larger classrooms, the severity of this problem becomes exacerbated even further, as teachers are forced to allocate valuable time for the purpose of marking attendance. The flourishing world of machine-learning based algorithms were the first solutions that we considered, and within the context of the premise, we concluded that facial recognition would likely be the most feasible and effective approach that we could use. This paper develops a mobile application to apply real time face recognition for the purpose of the above stated problem, using a combined backend of the Firestore database and Amazon AWS services. Applying our application to in person classrooms, the results show that our solutions are immensely effective in both saving time and reducing error.

Machine learning, Flutter, Facial Recognition.