Our inspiration for this project derives from a series of attempts to leverage and maximize time for the student body.
What It Does
Our application uses a client-server architecture and a systematic database system to take attendance through facial recognition -- an effective attendance and record-keeping method achieved through the use of Wolfram Alpha's machine learning and its neural networks's algorithms.
How We Built It
We built our project using Wolfram Alpha's machine learning's functions and algorithms to train a neural networks for advanced facial recognition. In addition, we used Python and SQLite in our backend to make the application scalable and easily implementable in environments other than school.
Challenges We Ran Into
Learning to program in Wolfram Alpha's Language turned out to be quite challenging --we had never been exposed to the syntax used, thus making it quite complex and difficult. Secondly, we found it quite hard to dynamically recognize faces [in a video form] as opposed to taking photos as each student signs in.
Accomplishments We're Proud Of
We are proud that on our first-ever hackathon project, we were able to pull through and learn a lot more than that we expected to.
What We Learned
Our learning experience has been tremendous! We have been able to learn a lot, ranging from different usage of technologies to attaining and strengthening out personal skills through out effective collaboration and planning. We pushed ourselves to learn on grounds we have never stepped on before, and we could not have been more proud.
What's Next for Student Attendance Facial Recognition
In the future, we plan to expand the application and add new features to further help maximize time not only for students, but also for professors --these include lateness tracking and much more.