This is a hackathon project developed during HackNY 2017 at Columbia University. The team members are Bruce Liu, Tom An, Tianyang Feng and Max Bai. We won the 1st Place and the Most Technical Award.


Police often find it hard tracking criminals with security cameras around the city, because of the low image quality they usually have. It is hard and exhausting for them to look at each frame and trying to identify a face. So what's a better way? There are a lot of things representing who we are: our face, finger print, voice... but one thing people might have been ignored is one's body's movement. We often have an experience identify a person from distance because of his or her walking pattern. Could that be possibly the next Face ID?

What it does

Identify a person based on his or her walking pattern.

How we built it

We build the movement recognition on Kinect, it tracks one's angular velocity and angular acceleration in every 2 seconds. We then write some helper functions that normalize the data, so that no matter which in which angle you are walking towards the camera, the movement's data will be the same. We want to use machine learning to train the neural network. But it is extremely hard for us to train a network from scratch in such a short time. So we decided to use transfer learning, which is way faster and can give us a high accuracy that could not possibly be achieved by building our own neural network. To use the inceptionV3 model, we wrote several functions that transform the information from the data to images effectively. Then we used transfer learning, rebuilt the last layer of the neural network, to finish up the whole thing.

Our training is based on a 10 minutes walk of 4 people, and the final identification accuracy is 86.7%, which is not bad and proved that people's walking pattern can indeed be potentially a new FaceID: BodyID.


Collected accurate data from Kinect sensor by utilizing various techniques such as smoothing out and noise reduction.

Accurately reflected the trend and information of one’s body movement in the picture generated, thus allowing accurate machine learning results.

Utilized deep learning module and trained the neural network with a success rate of 86.7%

Won the 1st Place and the Most Technical Award in HackNY 2017.

What's next for BodyID

1. Tracking the criminal: An efficient way for identifying some criminal on run through some low quality videos.

2. Smart Home: In your home, the door will be opened automatically as you walking towards the door.

3. Office: You will be checked in and verified identity as you walk through the security gate of your office building, saying "Hello Mr./Ms. ___"

We think the application of BodyID is wide because of it's low cost(any camera can easily see a person's figure) and easy implementation.