Building a face recognition API with face_recognition, Flask and PostgreSQL
A couple of months ago I read about PimEyes a service that allows for finding images across the web that contain a matching face. There’s a little controversy behind the service because it doesn’t vet its users strictly which means that the system can be abused.
This got me thinking about how such a technology was built, as the face recognition libraries I had seen in the past required loading in images to conduct a comparison and at the scale that Pimeyes would be operating on this would be really inefficient.
In order to return results across multiple faces quickly there would need to be some alternative means of carrying out this face comparison on the database layer.
After a bit of searching I found an excellent, but broken example using PostgreSQL’s cube extension that saves two vectors of the face’s descriptors and uses euclidean distance to return records that are above a certain threshold. After fixing the incorrect SQL statement I was able to save an image’s descriptors and then use another image’s to return the original image as a match.
How it works
The Python code makes use of a couple of libraries:
opencv— Used to read the image to be processeddlib— Used to load a Histogram of Oriented Gradients (HOG) face detector used to find faces in the uploaded images, returns a list of coordinates in the image for a…
