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Ear Classification using Machine Learning & Deep Learning 👂

This project is a comprehensive Machine Learning lab assignment focused on classifying ear images to identify different individuals. The notebook implements and compares multiple approaches for feature extraction and classification, including:

  • K-Nearest Neighbors (KNN) with raw pixel data
  • Principal Component Analysis (PCA) for dimensionality reduction
  • Artificial Neural Networks (ANN) from scratch and using TensorFlow/Keras
  • Feature extraction techniques: BSIF (Binarized Statistical Image Features) and LBP (Local Binary Patterns)

Dataset 📊

  • Type: Ear images (.bmp format)
  • Total samples: 493 images
  • Image dimensions: 180×50 pixels (RGB)
  • Classes: Multiple individuals (labeled by participant IDs like 001, 002, etc.)
  • Data split: 67% training, 33% testing (stratified by class)

Key Findings 📈

  • BSIF features achieved the highest accuracy (~95% with ANN)
  • PCA successfully reduced dimensionality while preserving ~98% variance
  • LBP alone showed poor performance with ANN (likely due to histogram features not being suitable for this network architecture)
  • ANN with PCA significantly outperformed raw pixel ANN (84.7% vs 72.4%)

Lessons Learned 🧠

  • Feature extraction techniques (BSIF, LBP) can dramatically impact classification performance
  • PCA is effective for reducing dimensionality while preserving information
  • ANN architecture must be tailored to the feature representation (pixel vs. histogram data)
  • Stratified sampling is essential for imbalanced multi-class problems

License 📝

This project is for educational purposes as part of a Master's lab assignment.

Contact ✉️

• Email: wissambadia4@gmail.com • LinkedIn: Badia Ouissam Lakas