<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Blog - Tag - Andrews Website</title><link>https://andrew.com/tags/blog/</link><description>Blog - Tag - Andrews Website</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Sun, 31 Aug 2025 12:00:00 +0000</lastBuildDate><atom:link href="https://andrew.com/tags/blog/" rel="self" type="application/rss+xml"/><item><title>Emotion Detection with Convolutional Neural Networks</title><link>https://andrew.com/posts/cnn_emotion_detection/</link><pubDate>Sun, 31 Aug 2025 12:00:00 +0000</pubDate><author>xxxx</author><guid>https://andrew.com/posts/cnn_emotion_detection/</guid><description><![CDATA[<h1 id="emotion-detection-with-convolutional-neural-networks">Emotion Detection with Convolutional Neural Networks</h1>
<h2 id="why">Why?</h2>
<p>To explore machine learning techniques for image classification</p>
<h2 id="what">What?</h2>
<p>OpenCV handles face detection. A CNN trained on FER2013 handles emotion classification. Chain them together for live video analysis.</p>
<h2 id="code">Code</h2>
<h3 id="setup-data">Setup data</h3>
<p>For training, we are using the fer2013 black and white emotion-labelled faces dataset. The raw data is available on Kaggle <a href="https://www.kaggle.com/datasets/msambare/fer2013" target="_blank" rel="noopener noreffer ">here</a>.</p>
<p>We load our data and apply transformations using the <code>ImageDataGenerator</code> class. Applying small transformations to our data prevents overfitting which helps when making predictions on new data.</p>]]></description></item></channel></rss>