<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Machine-Learning on Selvam Subbiah</title><link>https://selvamsubbiah.com/tags/machine-learning/</link><description>Recent content in Machine-Learning on Selvam Subbiah</description><generator>Hugo -- 0.154.3</generator><language>en-us</language><lastBuildDate>Sun, 28 Nov 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://selvamsubbiah.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Face Landmarks Detection using MediaPipe Face Mesh in TensorFlow.js - Part 3</title><link>https://selvamsubbiah.com/mediapipe-iris-detection-in-tensorflow-js/</link><pubDate>Sun, 28 Nov 2021 00:00:00 +0000</pubDate><guid>https://selvamsubbiah.com/mediapipe-iris-detection-in-tensorflow-js/</guid><description>&lt;p&gt;This article is the continuation of the &lt;a href="https://selvamsubbiah.com/mediapipe-facemesh-triangles-in-tensorflow-js-part-2/"&gt;previous article&lt;/a&gt;
on MediaPipe Face Mesh model in TensorFlow.js, where we looked at creating the &lt;a href="https://en.wikipedia.org/wiki/Triangle_mesh" target="_blank" rel="noopener noreferrer"&gt;triangle mesh&lt;/a&gt;
of the face using the model’s output. Here, we will look at detecting and tracking iris within the eyes using the &lt;a href="https://google.github.io/mediapipe/solutions/iris.html" target="_blank" rel="noopener noreferrer"&gt;MediaPipe Iris&lt;/a&gt;
model.&lt;/p&gt;
&lt;h2 id="1-iris-detection"&gt;1. Iris Detection&lt;/h2&gt;
&lt;p&gt;MediaPipe Iris model accurately estimates the iris landmarks within the eye and tracks it. Iris detection &amp;amp; tracking can be used in augmented reality to create avatars and also to determine the distance of the camera to the user.&lt;/p&gt;</description></item></channel></rss>