Facial Expression Recognition with Keras

4.5
913 个评分
提供方
Coursera Project Network
20,987 人已注册
在此免费的指导 项目中,您将:

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

在面试中展现此实践经验

Clock2 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

必备条件

Prior experience with Python programming, and a theoretical understanding of Neural Networks and Convolutional Neural Networks is required.

您要培养的技能

Deep LearningConvolutional Neural NetworkMachine LearningComputer Visionkeras

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

审阅

来自FACIAL EXPRESSION RECOGNITION WITH KERAS的热门评论

查看所有评论

常见问题

常见问题

还有其他问题吗?请访问 学生帮助中心