By Sherif | February 17, 2025
Have you ever wanted to build a face recognition app, only to realize that your computer starts wheezing like an asthmatic hamster under the processing load? Yeah, same. But fear not, because I’ve got you covered!
Today, I’m going to walk you through how I built a Django-based face recognition system that registers and recognizes faces from images without making my laptop beg for mercy. And yes, I hit some roadblocks along the way (which I’ll happily rant about), but I promise you’ll walk away from this with some solid knowledge. Here is the link to the project if needed > https://github.com/Sheriff-AA/facial_recognition_app
Face recognition is everywhere—your phone unlocks with your face, airports scan your mug to let you in, and sometimes, your phone even tries to judge you for grabbing that third slice of cake. I wanted to create a system that could:
Register a face by uploading an image.
Recognize that face in another image, even if taken at a different angle.
Store logs of recognized faces (because data is king, right?).
Work efficiently on large datasets (because what's life without some scalability?).
Process images quickly by resizing them first, so my system doesn’t breakdown when handling high-resolution images.
So, let’s jump in.
Django - The friendly web framework for perfectionists with deadlines.
Face_Recognition (dlib-based) - Handles face detection and encoding.
Pillow (PIL) - Helps with image resizing.
FAISS (Facebook AI Similarity Search) - Makes large-scale face matching actually fast.
OpenCV - Because real-time camera face recognition sounded fun (until I realized it meant debugging webcams at 3 AM).
To register a face, we upload an image, extract its face embedding, and store it in the database along with the original image (renamed to something readable, because “img_123129381.jpg” is just annoying).
Multiple faces in one image? I had to slap a warning: "I need one face here, not your entire family reunion."
No face detected? Another warning: "Your pet iguana doesn’t count as a human."
Large image sizes? Resizing to 800x800 pixels solved that before my system started crying.
And boom! Face registered. Now onto the fun part: recognizing faces.
The system takes an uploaded image, extracts the face encoding, and compares it against stored embeddings using FAISS (because looping through thousands of faces with raw NumPy arrays is a crime against humanity).
Multiple faces in one image? Needed to return multiple matches.
Slow search speed? Enter FAISS, which indexes and retrieves similar faces in milliseconds.
Bad lighting / angles? The model isn’t perfect (neither is my camera).
And just like that, our system finds matches in a snap!
Here are some fun and serious applications:
* Security systems (Office entry, school attendance, Skynet?)
* Retail analytics (VIP customer recognition, shoplifting prevention, tracking Karen’s return visits)
* Event check-ins (Why bother with QR codes when your face is your ticket?)
* Smart home authentication (“Sorry, I only let the cat in.”)
Absolutely! By:
* Using FAISS for fast face search.
* Storing embeddings in a scalable database (PostgreSQL or MongoDB).
* Running image processing on a separate worker (Celery, Redis, or AWS Lambda).
* Load balancing using Django REST + multiple servers.
If Google can do it, so can we (just with fewer zeros in our budget)!
Building this face recognition system was both fun and frustrating.
✅ I learned how to optimize image processing.
✅ I made face matching super-fast with FAISS.
✅ I accidentally built a system that could recognize me even on my worst hair days.
Would I do it again? Heck yeah! But maybe with more coffee next time. ☕
So, if you’re thinking about building a face recognition app, go for it. And if you run into problems, just remember: your computer is probably as confused as you are.
Until next time—keep coding, and may your face always be recognized!