python code: What: Face recognition.

Why: The scope of this project is: $($a$)$ to understand the computational challenges of a very popular biometric attribute, i$.$e$.,$ face; $($b$)$ to be able to search for your own sources and have a literature review; $($c$)$ to construct your own solution, possibly using existing methods, from your own definition of the problem.

How: This will be a biometric validation task.

$($a$)$ Collect a set of face images. Best way to do this is to find publicly available datasets. The number of subjects$/$classes is open $($any$),$ and the number of images per subject is also open, however, there should be more than $20$ subjects with more than $2$ images each.

$($b$)$ Find and use any method suitable for face recognition. You may use older methods or more recent deep Neural Network$-$based frameworks. For the latter you can use models pre$-$trained on the specific dataset you use.

$($c$)$ Pick any three images from the dataset, each from a different subject, as "unknown" query and show the recognition performance. Also, pick two subject images not in the dataset and show the validation result.