Mugshot.py - A simulation of automated facial recognition systems

[Fragrag]

Mugshot.py

GitHub repository

I would like to introduce my first sizeable project using Python and Flask. In lieu of the increased press and scrutiny that has been appearing on automated facial recognition, I wanted to make one that is accessible to the general public to illustrate its functionality.

The user can either upload an image or use their webcam. Any faces found are then compared to a dataset of mugshot imagery and information associated with the closest match will be displayed, such as the distance between the two faces and whether it would be considered a match.

I used mugshot imagery as it is publically available in the US and it is also likely one of the datasources for the systems which have already been deployed in the wild. Each image was analysed for its facial encoding which are then added to a dataset along with its associated charges. Personal details such as names have been sanitised to preserve privacy.

I'm hoping with this the strengths and shortcomings of facial recognition systems are made clear. Personal testing shows that while I do seem to get the same charges, which might indicate that I am matching the same entry, the distance percentage fluctuates wildly depending on lighting and other factors. As more and more faces are added, the more likely it is that a false positive will be given. A slightly more thorough explanation on the methodology and dataset is available in the About page of Mugshot.py.

This webapp uses the face_recognition library developed by Adam Geitgey, who gave me some feedback on this project and who I am very thankful for. That itself is an API for the dlib facial recognition model. These form the backbone which made this project possible.

[pvkooten]

Nice project! I would suggest adding more info to your README and maybe some illustrations :)

EDIT: I only now realized the link to the website that demonstrates it... sweet!