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WatchDag

##What it does Uses machine learning to recognize users and relevant information in real-time ##How we built it We used OpenCv to track and detect users faces in real-time, then we passed a snapshot of the user to Azure which then used machine learning to determine the identity of the user. This was possible by feeding Azure a pre-existing database of images. ##Challenges we ran into Installing OpenCv 3.10 Working with OpenCv Java in general due to poor documentation Dealing with quadruple nested API calls Sending octet file streams to Azure Dealing with poor facial recognition APIs ##Accomplishments that we're proud of How accurate and how quickly it can determine the identity of a user That we were able to finish a project like this in only 20 something hours How cool it looks ##What we learned We learned how to deal with facial recognition, more specifically how far it has come and how far it has yet to go. ##What's next for WatchDag Get our hands on a bigger database of faces to protect the world or destroy it We're aware this project is actually pretty dangerous so were going to burn the project

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