Facial Landmarks Detection

Overview

This project implements a facial landmark detection system using the Xception model, trained on the ibug dataset. The goal is to accurately detect facial landmarks from images and provide a user-friendly web application for real-time detection.

This model will support real-time inference for applications such as:

Face recognition
Emotion detection
Virtual makeup
Augmented reality (AR)

Learning Process

Dataset used

Commonly Used Datasets:
- iBUG 300-W: Thousands of images labeled with 68 facial landmarks (eyes, nose, mouth, jawline).
- Applications: Face recognition, emotion analysis, augmented reality.
- Link for the dataset : here

Procedure

Augmentation:
- Implemented a FaceLandmarksAugmentation class for tailored augmentation techniques:
  - Cropping
  - Random cropping
  - Random rotation
  - Color jittering
- Key parameters include image dimensions, brightness, and rotation limits.
- Methods like offset_crop and random_rotation adjust landmark coordinates accordingly.
Preprocessor:
- Initializes augmentation methods.
- Normalizes data.
Dataset Class:
- Inherits from Dataset.
- Handles image paths, landmark coordinates, and cropping information parsed from XML files.
- Splits data into training and validation sets.
- Utilizes DataLoader for batch processing.

Network Design

Architecture:
- Modular CNN with depthwise separable convolutions to improve efficiency.
- Entry Block: Initial feature extraction.
- Middle Blocks: Residual connections.
- Exit Block: Outputs facial landmark coordinates.
Enhancements:
- Batch normalization and LeakyReLU for better performance.
Training Loop:
- Runs for 30 epochs.
- Computes training loss and performs backpropagation.
- Updates weights using an optimizer.
- Validates model after each epoch.
- Saves checkpoints to prevent overfitting and retain the best model.

Results

GitHub