FaceLyft

Inspiration

As university students and soon to be graduates, we understand the financial strain that comes along with being a student, especially in terms of commuting. Carpooling has been a long existing method of getting to a desired destination, but there are very few driving platforms that make the experience better for the carpool driver. Additionally, by encouraging more people to choose carpooling at a way of commuting, we hope to work towards more sustainable cities.

What it does

FaceLyft is a web app that includes features that allow drivers to lock or unlock their car from their device, as well as request payments from riders through facial recognition. Facial recognition is also implemented as an account verification to make signing into your account secure yet effortless.

How we built it

We used IBM Watson Visual Recognition as a way to recognize users from a live image; After which they can request money from riders in the carpool by taking a picture of them and calling our API that leverages the Interac E-transfer API. We utilized Firebase from the Google Cloud Platform and the SmartCar API to control the car. We built our own API using stdlib which collects information from Interac, IBM Watson, Firebase and SmartCar API's.

Challenges we ran into

IBM Facial Recognition software isn't quite perfect and doesn't always accurately recognize the person in the images we send it. There were also many challenges that came up as we began to integrate several APIs together to build our own API in Standard Library. This was particularly tough when considering the flow for authenticating the SmartCar as it required a redirection of the url.

Accomplishments that we're proud of

We successfully got all of our APIs to work together! (SmartCar API, Firebase, Watson, StdLib,Google Maps, and our own Standard Library layer). Other tough feats we accomplished was the entire webcam to image to API flow that wasn't trivial to design or implement.

What's next for FaceLyft

While creating FaceLyft, we created a security API for requesting for payment via visual recognition. We believe that this API can be used in so many more scenarios than carpooling and hope we can expand this API into different user cases.

Built With

Submitted to

UofTHacks VI
- Winner 2nd Place
- Winner Best hack or app using the Interac e-Transfer Open API
- Winner Top 10 Prize (Entrepreneurship Hatchery)

Created by

I worked on training IBM's Watson API Vision Recognition software with a minimum of ten faces and integrating the API into the project. This also involved creating the frontend webcam logistics that would have to take a picture and send that picture to the API. I also helped with integrating the different APIs into the user's dashboard so their personal information and smart car details were displayed.

Kyra S
Software engineer / Jamaican
I mainly worked on the front-end using HTML and CSS. I designed most of the layout for the landing page as well as the dashboard. This was my first hackathon and also my first time using HTML and CSS in a project.

Lisa Tran
Chemical & Bio-Engineering Student at McMaster University
I worked on developing our own API (using STDLIB) and ensured that it integrated seamlessly with the other API's including Interac's Money Request API. I also helped with the front-end, grabbing the necessary data from our endpoints and using it in the front-end where we needed it. A majority of my time was spent making sure Interac's Money Request endpoints for requesting money with sms/email notifications, retrieving contacts and checking for the status of e-transfers from our FaceLyft dashboard. I also helped with testing the SmartCar API with a Tesla Model S 2019 and was responsible for demoing FaceLyft during the judging round.

Asseel
I was primarily tasked with making sure the integration of all the systems we had were perfect. We had a lot of "moving parts" to this project, especially when trying to keep it dynamic by building and using our own API. I took charge of bringing together the Interac, SmartCar, and IBM Watson APIs and getting them to work together to produce the results required by our application. I also helped in making the client and server side back end which makes calls to our API and a few others.

Amr Mahmoud

Updates

Kyra S started this project — Jan 20, 2019 01:42 AM EST

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