With classes as short as 1 hour to cover an entire week's worth of lecture topics along with further clarifications, time passes by very quickly. With iSeeU, the main cause of disruption and delay - attendance taking - can be eradicated, enabling a more conducive environment for learning. Furthermore, it encourages physical presence in class that provides an incentive to be consistently motivated.

What it does

It adopts facial recognition technology to identify each student's face and mark his or her attendance after registering themselves. An Excel attendance list will be then generated for the teacher's reference and each student who attended will be sent a confirmation email to assure them that their attendance has been taken.

How we built it

Python, openCV, tkinter

Challenges we ran into

Tackling and trying to integrate computer vision in our application proved to be difficult, that too while learning python when all of us are java programmers.

Accomplishments that we're proud of

Despite several setbacks in the beginning where we faced continual failures, we did not give up and managed to come up with an end product that we are proud of while gaining immense knowledge and insight into the artificial intelligence and computer vision aspect of computer science.

What we learned

We learnt how to integrate all the different technologies together into a singular working application. The interconnectedness of technology such as how computer vision from openCV library can be seamlessly integrated with everyday tools such as excel in such an efficient manner truly amazed us.

What's next for iSeeU

We hope to improve the accuracy of the facial recognition such that there will always be at least a 90% match for each face identified. This can be done with more datasets and a larger pool of registered participants.

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