## Inspiration

How long does class attendance take? 3 minutes? With 60 classes, 4 periods a day, and 180 school days in a year, this program will save a cumulative 72 days every year! Our team recognized that the advent of neural networks yields momentous potential, and one such opportunity is face recognition. We utilized this cutting-edge technology to save time in regards to attendance.

## What it does

The program uses facial recognition to determine who enters and exits the room. With this knowledge, we can keep track of everyone who is inside, everyone who is outside, and the unrecognized people that are inside the room. Furthermore, we can display all of this on a front end html application.

## How I built it

A camera that is mounted by the door sends a live image feed to Raspberry pi, which then transfers that information to Flask. Flask utilizes neural networks and machine learning to study previous images of faces, and when someone enters the room, the program matches the face to a person in the database. Then, the program stores the attendees in the room, the people that are absent, and the unrecognized people. Finally, the front end program uses html, css, and javascript to display the live video feed, the people that are attending or absent, the faces of all unrecognized people.

## Challenges I ran into

When we were using the AWS, we uploaded to the bucket, and that triggered a Lamda. In short, we had too many problematic middle-men, and this was fixed by removing them and communicating directly. Another issue was trying to read from cameras that are not designed for Raspberry pi. Finally, we accidentally pushed the wrong html2 file, causing a huge merge conflict problem.

## Accomplishments that I'm proud of

We were successfully able to integrate neural networks with Flask to recognize faces. We were also able to make everything much more efficient than before.

## What I learned

We learned that it is often better to directly communicate with the needed software. There is no point in having middlemen unless they have a specific use. Furthermore, we also improved our server creating skills and gained many valuable insights. We also taught a team member how to use GIT and how to program in html.

## What's next for Big Brother

We would like to match inputs from external social media sites so that unrecognized attendees could be checked into an event. We also would like to export CSV files that display the attendees, their status, and unrecognized people.
## What it does

Tickets is a secure, affordable, and painless system for registration and organization of in-person events. It utilizes public key cryptography to ensure the identity of visitors, while staying affordable for organizers, with no extra equipment or cost other than a cellphone. Additionally, it provides an easy method of requesting waiver and form signatures through Docusign.

## How we built it

We used Bluetooth Low Energy in order to provide easy communication between devices, PGP in order to verify the identities of both parties involved, and a variety of technologies, including Vue.js, MongoDB Stitch, and Bulma to make the final product.

## Challenges we ran into

We tried working (and struggling) with NFC and other wireless technologies before settling on Bluetooth LE as the best option for our use case. We also spent a lot of time getting familiar with MongoDB Stitch and the Docusign API.

## Accomplishments that we're proud of

We're proud of successfully creating a polished and functional product in a short period of time.

## What we learned

This was our first time using MongoDB Stitch, as well as Bluetooth Low Energy.

## What's next for Tickets

An option to allow for payments for events, as well as more input formats and data collection.
## Inspiration

I've tutored students for many years and very early on, and it quickly became evident that these students were incredibly bright, but they just never asked for clarification in class. Often in tutoring sessions, I am stuck wondering whether a student is confused or not and oftentimes it hinders the progression of the class. I can only wonder how it must be when it is not just a one on one interaction.

## What it does

1. Automatically takes attendance through facial recognition software using only one image. For deployment purposes, the image could be just a yearbook photo which nearly everyone takes.
2. Scans the emotions and facial expressions of the students in the classroom and tells the teacher when the majority of the students are confused.

## How I built it

For the frontend, a MVVM structure was created with UIKit in swift. The backend was built with python and flask. The facial recognition model came from openCV and the emotion recognition model was the VGG19 model trained on the FER-13 dataset.

## Challenges we ran into

The first main challenge was using flask for the backend because I had never done it before but it quickly became evident that it wasn't very difficult and was quite similar to HTTP requests in JavaScript. The next main challenge was getting both AI algorithms to run in tandem with each other and produce a combined output that could eventually be extracted with the HTTP requests. The final hurdle was creating all the niche features like tracking all the total time the students are in the classroom and warning the teacher about each student that is skipping class.

## Accomplishments that we're proud of

Building a fully functioning, ready to deploy app that can be used in public schools today in under 24 hours.

## What's next for teach.ai

I plan on trying to take this company forward. This is not just some idea I had for only a hackathon, but rather, my vision is much greater. This tool would be extremely useful all accross the country and could cause extreme changes in how our future generations grow and develop their minds. I plan on building a few fully working prototypes and seeing if I can try and deploy them in the schools near my house. From there I can build a reputation and scale the company further.