Facial Keypoint Detection

Project Overview

In this project, you’ll combine your knowledge of computer vision techniques and deep learning architectures to build a facial keypoint detection system. Facial keypoints include points around the eyes, nose, and mouth on a face and are used in many applications. These applications include: facial tracking, facial pose recognition, facial filters, and emotion recognition. Your completed code should be able to look at any image, detect faces, and predict the locations of facial keypoints on each face.

Project Instructions

The project will be broken up into a few main parts in four Python notebooks, only Notebooks 2 and 3 (and the models.py file) will be graded:

Notebook 1 : Loading and Visualizing the Facial Keypoint Data

Notebook 2 : Defining and Training a Convolutional Neural Network (CNN) to Predict Facial Keypoints

Notebook 3 : Facial Keypoint Detection Using Haar Cascades and your Trained CNN

Notebook 4 : Fun Filters and Keypoint Uses

You can find these notebooks in the Udacity workspace that appears in the concept titled Project: Facial Keypoint Detection. This workspace provides a Jupyter notebook server directly in your browser.

Note that this project does not require the use of GPU, so this project does not include instructions for GPU setup.

You can also choose to complete this project in your own local repository (and may use GPU, there), and you can find all of the project files in this GitHub repository. Note that while you are allowed to complete this project on your local computer, you are strongly encouraged to complete the project from the workspace.

Evaluation

Your project will be reviewed by a Udacity reviewer against the Facial Keypoint Detection project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass.

Zip file submission

If you are submitting this project from a workspace or as a zip file, make sure your file is not too large. You are only graded on Notebooks 2, 3, and the file models.py. Please delete any model checkpoints you have saved in saved_models/ and any large image data you have saved in the workspace home directory before you submit!

Project Submission Checklist

Before submitting your project, please review and confirm the following items.
I am confident all rubric items have been met and my project will pass as submitted.
Project builds correctly without errors and runs.
All required functionality exists and my project behaves as expected per the project's specifications.

Once you have checked all these items, you are ready to submit!

Ready to submit your project?

Click on the "Submit Project" button and follow the instructions to submit!