Face-Recognition-using-Unsupervised-Semi-supervised-ML

Face Recognition Algorithm using Unsupervised and Semi-Supervised Techniques

The Olivetti faces dataset:

This dataset contains a set of face images taken between April 1992 and April 1994 at AT&T Laboratories Cambridge. The sklearn.datasets.fetch_olivetti_faces function is the data fetching / caching function that downloads the data archive from AT&T.

The target for this database is an integer from 0 to 39 indicating the identity of the person pictured; however, with only 10 examples per class, this relatively small dataset is more interesting from an unsupervised or semi-supervised perspective.

Below tasks are covered in the project:

• Apply various unsupervised techniques and compare what works best for the data
• Apply pca, kmeans as a preprocessing
• Determine the optimal number of clusters and plot the cluster of faces
• Take the test data and create labels for the test data using the semi-supervised method
• Have a validation dataset to verify the created labels are close to the validation test labels before propagating them into the test set
• Final Result: Accuracy increased from 73% to 80% by propagating labels only to the instances that are really close to the centroid