Arpan-Mishra / Selfie-Filter-Project

Creating Selfie Filters using facial keypoint detection and OpenCV

Selfie-Filter-Project

Creating Selfie Filters using facial keypoint detection and OpenCV

Methods Used

• Convolutional Neural Networks
• Deep Learning
• Visualisation
• Regression
• Statistics

Technologies

• Python
• Pandas, Jupyter
• Numpy
• Keras
• OpenCV
• HAAR Cascade Classifier
• Matplotlib

Description

• The aim of this project was to create selfie filters using facial keypoint detection.
• I have taken the data from Kaggle consisting of 7049 images with 15 keypoints per image.
  

Process

• Using a Convolutional Neural Network Architecture inspired by the NaimishNet we predict the facial keypoints and save the model. The final model has a validation Mean Absolute Deviation of 1.28.
• Then with OpenCV we access our webcam and use HAAR Cascade Frontal Face classifier to detect the face in the frame.
• The detected face is pre-processed and fed into our saved model to get the keypoint coordinates.
• Using these keypoint coordinates we place our filter images. Here we have used 4 keypoints to achieve our desired result.
• The edited image is then shown in the Filter frame [Left]. The detected keypoints can be scene in the Keypoint Detector frame [Right].

Credits

• Data Source: https://www.kaggle.com/c/facial-keypoints-detection/data
• CNN Architecture: https://arxiv.org/pdf/1710.00977.pdf
• HAAR Cascade Classifier Tutorial: https://www.youtube.com/watch?v=LopYA64KmdE

Contact

• For any queries and feedback please contact me at [e-mail]

Note: The project is only for education purposes, no plagiarism is intended.