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AMLSassignment

Applied Machine Learning Systems ELEC0132 Assignment

Maryam Habibollahi (SN: 15000241)

Assignment tasks

  • Detection and removal of noisy images
  • Training, validation and testing subsets division
  • Train ML models to perform
    • Binary
    1. Emotion recognition (smile/!smile)
    2. Age identification (young/old)
    3. Glasses detection (with/without)
    4. Human detection (real/avatar)
    • Multiclass
    1. Hair colour recognition (ginger, blond, brown, grey, black, bald)

Dataset folder

How to compile and use code

Required libraries

  • Classification models: Scikit-learn, Keras, dlib, and OpenCV.

  • Data processing, storage, and representation: NumPy, Pandas, Time, OS, and MatplotLib.

Python files

  • landmarks.py

Implements funtions on Haar Cascode, HOG, and Deep Learning-based face detectors for obtaining image landmarks from detected faces.

  • classification.py

Includes the required functions for SVM and MLP implementation, as well as cross-validation testing.

  • utils.py

Provides the utility functions for handling files and data used in landmarks.py and classification.py.

  • testing.py

Calls the detection and classification functions and stores the results to csv. This file includes one function for binary tasks 1-4 and two for multiclass task 5. The landmarks previously stored in out/ are used by default to save time. To re-run the face detector, uncomment function update_features()

  • models/lenet.py

Implements the LeNet architecture for the multiclass classification task

Folders in Python

  • code/out/

Includes all the results from the tests (see out/README.md) Face detector features are stored in Face_detection/ for convenience

  • code/models/

Contains face detection models and the LeNet architecture setup file

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