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Facial Keypoint Detection

Project Overview

This is an project from udacity computer vision nanodegree. In this project, I have used 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. My model 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.

Facial Keypoint Detection

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

Local Environment Instruction## Project Instructions

  1. Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.
git clone https://github.com/Apucs/Facial-Keypoint-Detection.git
cd Facial-Keypoint-detection
  1. Create (and activate) a new environment, named cv-nd with Python 3.6. If prompted to proceed with the install (Proceed [y]/n) type y.

    • Linux or Mac:
    conda create -n cv-nd python=3.6
    source activate cv-nd
    
    • Windows:
    conda create --name cv-nd python=3.6
    activate cv-nd
    
  2. Install PyTorch and torchvision; this should install the latest version of PyTorch.

    • Linux or Mac:
    conda install pytorch torchvision -c pytorch 
    
    • Windows:
    conda install pytorch-cpu -c pytorch
    pip install torchvisionhttps://github.com/Apucs/Facial-Keypoint-Detection.git
    
  3. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).

pip install -r requirements.txt

Model Architecture

Layer name Layer shape
Input 1,224,224
Convolutional2d_1 32,221,221
activation 32,221,221
Maxpooling2d_1 32,110,110
Dropout_1 32,110,110
Convolutional2d_2 64,108,108
activation 64,108,108
Maxpooling2d_2 64,54,54
Dropout_2 64,54,54
Convolutional2d_3 128,53,53
activation 128,53,53
Maxpooling2d_3 128,26,26
Dropout_3 128,26,26
Convolutional2d_4 256,26,26
activation 256,26,26
Maxpooling2d_4 256,13,13
Dropout_4 256,13,13
Flatten 43264
Dense_1 1024
Activation 1024
Dropout_5 1024
Dense_2 512
Activation 512
Dropout_6 512
Dense_3 136

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

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