Okay, so let me tell you about this little project I messed around with called “faces northampton.” It was a fun little deep dive, and I learned a bunch. Buckle up, it’s story time!
It all started with me just kinda poking around online, looking for something interesting to play with. I stumbled upon some open-source facial recognition libraries. Thought to myself, “Hey, Northampton’s got a lot of CCTV cameras, wonder what I could do with that?” Obviously, I wasn’t gonna start tapping into city surveillance or anything illegal. This was purely a thought experiment, okay?
First thing I did was downloaded a bunch of example code. It was mostly Python, which is my go-to for quick and dirty projects. Then I installed all the necessary libraries, OpenCV, face_recognition, all that jazz. That part’s always a bit of a pain, getting all the dependencies right, but nothing too crazy.
Next, I found a publicly available dataset of faces. It wasn’t anything specific to Northampton, just a generic dataset for training facial recognition models. I cleaned it up a bit, got rid of blurry images and duplicates, you know, the usual data wrangling stuff.
Then came the fun part: training the model. I fed the cleaned dataset into the face_recognition library, and it started churning away. It took a few hours on my laptop, which isn’t exactly a supercomputer. After it was done training, I saved the model to a file.
Now, here’s where it got interesting. I grabbed a few publicly available images of people from Northampton – councillors, local celebrities, that sort of thing. Just stuff I could easily find with a quick Google search. No stalking involved, I promise!
I wrote a script to load the trained model and then run it on those images. The script would detect faces in the image, compare them to the faces the model was trained on, and output the name of the closest match. Pretty standard stuff, really.
The results were… mixed. It correctly identified some of the people, which was pretty cool. But it also made some hilarious mistakes. Like, it thought one of the councillors looked like a famous actor. I think it was the beard.
I tweaked the model a bit, adjusted the confidence thresholds, and ran it again. The accuracy improved slightly, but it was still far from perfect. Which is totally expected, to be honest. You need a massive dataset to get really good facial recognition accuracy.
So, “faces northampton” wasn’t exactly going to replace the police force anytime soon. But it was a fun exercise. I learned a lot about facial recognition, machine learning, and the limitations of these technologies. And it was a good reminder that even the fanciest algorithms can still make silly mistakes.
What I realized during the project was the huge ethical implications and the potential problems with the data and the bias that could creep in. I thought about privacy and that I shouldn’t really be working with peoples faces without express permission so I just stopped and deleted all the code
Would I do it again? Probably. But maybe with a different dataset and a clearer goal. And definitely with a lot more caution about the ethical implications. That’s the main thing I took away from the project.
