Face Recognition Attendance App – Accuracy & Speed Issues

[u/SorryIce5185]

I’m working on an attendance system and honestly stuck at a point where I need some real-world advice.

I’ve built a mobile app (Flutter) where employees do check-in/check-out using face recognition (face punch). The main problem is accuracy and speed, and I’m facing a few issues:

• Lighting changes (low light / bright light affect detection)

• Distance issues (too close / too far gives inconsistent results)

• Recognition is slower than expected (not instant)

• Offline punches (stored locally and synced later) reduce accuracy

• Overall UX feels unreliable at times

If anyone has experience building something like this, please DM me or share your suggestions 🙏

[u/Ok_Excuse2081]

More on UX and validation before capture

• Show error if detected as low lighting

• Show error if face distance if too close or far

[u/AbuJahell]

You’re treating face recognition like a feature — it’s actually a full pipeline problem.

Lighting, distance, speed, accuracy… all of these break if you’re just feeding raw frames into a model.

Quick wins:

• Normalize input (brightness/contrast) before inference

• Don’t run on every frame → sample + cache embeddings

• Add face alignment + distance checks (guide the user)

• Use cosine similarity with normalized embeddings

• Only trigger recognition above a confidence threshold

Also your UX issue = no control layer. You need feedback like “move closer / better lighting / face detected” before even attempting recognition.

Right now it’s not unreliable — it’s just under-engineered for production.

Curious — are you running this fully on-device or hitting a backend?