We're building a Face Recognition platform that lets you quickly integrate human identity features into your products and services—it's speedy, safe, and secure.Kairos is a simple concept—you submit images into our API, and our deep learning algorithms analyze the faces found, then the API returns a bunch of useful data about the faces we find. You can use this to search, match and compare faces, or measure characteristics such as age, and gender.
This is a demo of face substitution using the javascript library clmtrackr. Keep your face still until the facemodel has fitted and try out different masks from the dropdown
This is an example of precise face-tracking in an image using the javascript library clmtrackr. To try it out, simply click start.To try it out with your own image, choose a file above by clicking "choose file". If the tracking has problems, try selecting the face in the image manually by clicking "manually select face", and click and hold to drag a square around the face in the image.The image is from the FG-net Talking Face project
[Edit, June 2016: To the best of my knowledge, this work with Arturo Castro is the first example of realistic "face swapping" in realtime. If you're interested in experimenting with the technique, I recommend Audun's implementation here auduno.github.io/clmtrackr/examples/facesubstitution.html ]
clmtrackr is a javascript library for fitting facial models to faces in videos or images. It currently is an implementation of constrained local models fitted by regularized landmark mean-shift, as described in Jason M. Saragih's paper. clmtrackr tracks a face and outputs the coordinate positions of the face model as an array, following the numbering of the model below:
Many machine learning systems look at some kind of complicated input (say, an image) and produce a simple output (a label like, "cat"). By contrast, the goal of a generative model is something like the opposite: take a small piece of input—perhaps a few random numbers—and produce a complex output, like an image of a realistic-looking face. A generative adversarial network (GAN) is an especially effective type of generative model, introduced only a few years ago, which has been a subject of intense interest in the machine learning community.
Kairos: Face Recognition API Reference Docs
Face substitution
Face Tracker
Face Substitution on Vimeo
auduno/clmtrackr: Javascript library for precise tracking of facial features via Constrained Local Models
shaoanlu/faceswap-GAN: A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.
GAN Lab: Play with Generative Adversarial Networks in Your Browser!
face-api.js