In this project, I combined my 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. the completed code is able to look at any image, detect faces, and predict the locations of facial keypoints on each face; examples of these keypoints are displayed below.

The project was broken up into a few main parts in four Python notebooks, only Notebooks 2 and 3 (and the models.py file) contains the code built:

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

All of the starting code and resources you'll need to compete this project are in the following Github repository. Before you can get started coding, you'll have to make sure that you have all the libraries and dependencies required to support this project. If you have already created a cv-nd environment for exercise code, then you can use that environment!

LICENSE: This project is licensed under the terms of the MIT license.