InVein

Inspiration

What it does

To obtain an accurate vein image, we first make use of the fact that hemoglobin in the blood absorbs infrared light. Taking infrared LEDs in combination with an IR camera connected to a Raspberry Pi, we can capture and process an image of the palm using OpenCV. Once we have a noise reduced, skeletonized image of the vein network, we use TensorFlow models to train the network with training data. If we have a match, we display the result with a Node.js web app, and a servo unlocks the door!

How I built it

Challenges we ran into

We ran into challenges collecting data to train our neural network (which we thank 15 HTN participants for providing!), image processing, and building an effective locking mechanism.

Accomplishments that I'm proud of

We love the fact that we were able to minimize the cost of a usually expensive authentication system!

What we learned

We learned about how to use neural networks effectively with minimal data, how to properly structure code across a stack that ranges from embedded to frontend, and how to work wood!

What's next for InVein

Working on getting a more accurate classification system through larger training data sets.

Built With