Mediapipe on Selvam Subbiah

Face Landmarks Detection using MediaPipe Face Mesh in TensorFlow.js - Part 3

Sun, 28 Nov 2021 00:00:00 +0000

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.

Facial Landmarks Detection using MediaPipe Face Mesh in TensorFlow.js - Part 2

Sun, 21 Nov 2021 00:00:00 +0000

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.

Facial Landmarks Detection using MediaPipe Face Mesh in TensorFlow.js

Wed, 17 Nov 2021 00:00:00 +0000

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.

TensorFlow.js - Cricket Shot classification using Pose Detection

Sat, 13 Nov 2021 00:00:00 +0000

In this article, we will look at how to use Pose Detection in TensorFlow.js to classify cricket shots. In the game of cricket, there are many different shots played, such as cover drive, square cut, flick, sweep, straight drive, etc.

In this example, we will attempt to classify three cricket shots - cover drive, flick and square cut. We will utilize BlazePose TensorFlow.js model to obtain the poses from images of players playing the above shots. We will then feed these poses data to a KNN classifier to classify between these three shots.

Pose Detection using BlazePose in TensorFlow.js

Mon, 11 Oct 2021 00:00:00 +0000

Human Pose Detection is fascinating as there are endless use cases and opens up the possibility for various kinds of applications which were earlier either difficult to build or required specialized hardware and software.

Photo by Alex Shaw on Unsplash

In this article, I will walk you through the steps required to build a simple JavaScript app that detects human poses using Pose Detection API in TensorFlow.js utilizing the Mediapipe BlazePose model.