I built a 13 MB open-source face verification model because paid APIs felt ridiculous
I wanted to add face verification to my startup, SwayamWhere.com.
Then I looked at the pricing for face verification APIs.
Around $1 to $1.50 per 1,000 images/API calls sounds cheap at first, but once you factor in onboarding, duplicate profile checks, retries, testing, abuse prevention, and scale, it becomes a recurring tax on your trust layer.
So I decided to build my own.
After 2 months of training, testing, threshold tuning, false accept reduction, embedding comparison, model packaging, and documentation, I’m open-sourcing it.
It’s called TinyFaceMatch.
It is a lightweight, MIT-licensed face verification model that compares two aligned face images and returns a match decision with similarity scores.
Current benchmark:
- Accuracy: 99.72%
- ROC AUC: 0.9983
- Balanced accuracy: 99.02%
- True accept rate: 98.30%
- False accept rate: 0.25%
- False reject rate: 1.70%
- Model size: 13.238 MB
- Embedding size: 128-D
- License: MIT
The main goal was not to create another huge research model.
The goal was to create something small enough to actually ship.
For context:
- OpenCV SFace reports 99.60% LFW accuracy with a 36.9 MB recognition model.
- dlib face recognition reports 99.38% LFW accuracy.
- FaceNet VGGFace2-style models report around 99.65% LFW accuracy, but can be around 107 MB.
TinyFaceMatch reaches 99.72% accuracy in a 13.238 MB package.
No paid API call per verification.
No vendor lock-in.
No heavyweight deployment.
No separate commercial license needed.
I built this because I wanted face verification that was practical, local-first, auditable, affordable, and open.
Repo:
https://github.com/yuvrajraina/tinyfacematch
Docs and demo:
https://tinyfacematch.yuvrajraina.com/
Would love feedback from anyone working on computer vision, identity, trust and safety, or lightweight ML deployment.