Facial Expression Recognition with Keras
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
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
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:
Introduction and Overview
Explore the Dataset
Generate Training and Validation Batches
Create a Convolutional Neural Network (CNN) Model
Train and Evaluate Model
Save and Serialize Model as JSON String
Create a Flask App to Serve Predictions
Create a Class to Output Model Predictions
Design an HTML Template for the Flask App
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
Reviews
TOP REVIEWS FROM FACIAL EXPRESSION RECOGNITION WITH KERAS
All the concepts are well explained. The project gives a nice insight about how we can integrate different ML frameworks to build a project and also how to deploy the model as a web app by Flask.
This is a great hands-on project ! It is very well designed, and the instructor guides you to do it step by step. I enjoy this learning and practicing process a lot. Thank you !
Very easy to follow and the instructor was very informative throughout the project. As a beginner myself, it was easy for me to follow along and understand the project
This project gave me complete knowledge for implementiing the face recognition in future.This help me to built an app using FLASK.Its a good project to start with.
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