Face-Mesh

This article is the continuation of the previous article on MediaPipe Face Mesh model in TensorFlow.js, where we looked at creating the triangle mesh of the face using the model’s output. Here, we will look at detecting and tracking iris within the eyes using the MediaPipe Iris model. 1. Iris Detection MediaPipe Iris model accurately estimates the iris landmarks within the eye and tracks it. Iris detection & tracking can be used in augmented reality to create avatars and also to determine the distance of the camera to the user. ...

This article is the continuation of the previous article on MediaPipe Face Mesh model in TensorFlow.js, where we looked at the basic usage of this model. Here, we will look at how to connect the 468 3D facial landmark predictions as multiple triangles to create a triangle mesh of the face. 1. Triangle Mesh To create a triangle mesh from the 468 [x,y,z] coordinates, we will need to identify which are the set of points that need to be connected to form triangles. Luckily Google has provided those indexes for us to draw the triangle mesh. It is present in the triangulation.js file located in the Github repo of the pre-trained model. ...

In this article, we will walk through an example to identify facial landmarks using the state of the art MediaPipe Face Mesh model . This model is present in the Face Landmarks Detection package in TensorFlow .js library’s pre-trained model suite. This is part 1 of a series of articles in which we dive into the details of the Face Mesh model and its usage. 1. Overview The MediaPipe Face Mesh model estimates 468 3D facial landmarks in real time covering the overall surface geometry of a human face. It also supports Iris detection that accurately tracks the iris within the eye. Iris detection when enabled provides an additional set of 10 landmarks - 5 points for each eye. ...