In this project, I built 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; examples of these keypoints are displayed below.
The project will be broken up into a few main parts in four Python notebooks,
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 data you'll need to train a neural network is in the P1_Facial_Keypoints repo, in the subdirectory data. In this folder are training and tests set of image/keypoint data, and their respective csv files. This will be further explored in Notebook 1: Loading and Visualizing Data, and you're encouraged to look trough these folders on your own, too.
LICENSE: This project is licensed under the terms of the MIT license.