OpenCV Facial Filters

Overview

This project demonstrates the creation of Snapchat-like facial filters using Deep Learning and OpenCV. It involves facial keypoint detection to superimpose themed filters on a face in real time. I came up with the idea for the project on 21 December 2018, Hence the Christmas theme :P.

Detailed methodology and insights can be found in this Medium article.

Dataset

Utilized the Facial Keypoints Detection dataset from Kaggle.

Methodology

• Data Augmentation: Flipped images and key points for diversity.
• Model Architecture:
  • CNN: Acts as a feature extractor.
  • ANN: A fully connected network for facial keypoint regression.
• Training:
  • Loss Metric: Mean Absolute Loss.
  • Performance: Achieved ~0.0113 after 300 epochs with the Adam optimizer.
• Implementation:
  • Real-time Data Capture: Used OpenCV for live webcam feed.
  • Preprocessing: Standardized input before feeding into the model.
  • Output Utilization: Keypoint positions determined the placement and scale of thematic filters.