Facial Expression Recognition with Keras

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In this Guided Project, you will:

Develop a facial expression recognition model in Keras

Build and train a convolutional neural network (CNN)

Deploy the trained model to a web interface with Flask

Apply the model to real-time video streams and image data

2 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

In this 2-hour long project-based course, you will build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces. The objective is to classify each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). You will use OpenCV to automatically detect faces in images and draw bounding boxes around them. Once you have trained, saved, and exported the CNN, you will directly serve the trained model to a web interface and perform real-time facial expression recognition on video and image data. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • Deep Learning

  • Convolutional Neural Network

  • Machine Learning

  • Computer Vision

  • keras

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction and Overview

  2. Explore the Dataset

  3. Generate Training and Validation Batches

  4. Create a Convolutional Neural Network (CNN) Model

  5. Train and Evaluate Model

  6. Save and Serialize Model as JSON String

  7. Create a Flask App to Serve Predictions

  8. Create a Class to Output Model Predictions

  9. Design an HTML Template for the Flask App

  10. Use Model to Recognize Facial Expressions in Videos

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.

Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

Guided Projects are not eligible for refunds. See our full refund policy.

Financial aid is not available for Guided Projects.

Auditing is not available for Guided Projects.

At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.