Shivamyadav's avatar

Laravel Smart Attendance System with Live Geofencing (10–20 Meter Radius) β€” Feedback Needed

I’m building a lightweight Laravel-based attendance system where users can check in/out only when they are physically present within a strict 10–20 meter radius of the workplace using live GPS validation.

  1. βš™οΈ Core Features Implemented 2.πŸ“ Real-time geofencing (strict 10–20m radius enforcement)
  2. 🎯 GPS accuracy filtering (reject low-accuracy readings)
  3. ⏱️ Live check-in only (no delayed or backdated submissions)
  4. 🚫 Mock location detection (Android-based checks)
  5. πŸ“± Device tracking (basic fingerprinting for consistency)
  6. 🀳 Selfie Verification Approach (No AI)

To keep the system lightweight and privacy-friendly, I’m intentionally avoiding AI/ML solutions.

Current approach:

  1. Capture a selfie during check-in
  2. Compare it with a previously stored reference image
  3. Use pixel-level comparison techniques, such as:
    1. Image difference
    2. Hashing (e.g., perceptual hash)
    3. Similarity scoring

❓ Feedback I’m Looking For

I’d really appreciate insights from experienced developers on the following:

  1. Reliability How reliable is pixel-based image comparison in real-world usage?
  2. Real-world Variations How should I handle: 1. lighting differences 2. face angle changes 3. different camera quality
  3. Alternatives (Without AI) Are there any better non-AI approaches for basic face verification?
  4. Feasibility Is this approach fundamentally flawed for identity verification?
  5. Improvements Would capturing multiple selfies (2–3 per check-in) improve accuracy or just add noise? 🎯 Goal

The aim is to build a system that is:

  1. βœ… Simple to integrate
  2. ⚑ Lightweight (no heavy dependencies)
  3. πŸ”’ Privacy-friendly (no AI processing)
  4. πŸ›‘οΈ Reasonably resistant to misuse

πŸ™ Closing

Any suggestions, critiques, or alternative approaches would be greatly appreciated. Looking forward to learning from your experience!

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7 replies
imranbru's avatar

Honestly, I’d steer clear of pixel-level comparison or pHash for face verification it’s going to be a support nightmare because even a slight change in lighting or a head tilt will trigger a false rejection. Since you want to keep things lightweight and AI-free, your best bet is to treat the selfie as a "visual receipt" for human audit rather than an automated lock; just force a live camera capture (no gallery uploads) and let the threat of a manager spot-checking the logs act as the deterrent. Also, definitely keep an eye on that 10–20m geofence it's incredibly tight for real-world GPS drift, especially indoors, so you might want to allow a small buffer or flag "near-misses" for review instead of flat-out rejecting them.

Shivamyadav's avatar

Yeah, from gpt.

I told him that this is what I was thinking is there any security vulnerabilities that can occurs and write a thread post to get the feedback from the professional and senior to the it field.

DigitalArtisan's avatar

And you state I’m intentionally avoiding AI/ML solutions.

You started with an AI.

Shivamyadav's avatar

Not started just asked S better version of my question to get better help from people πŸ™πŸ˜”

martinbean's avatar

@shivamyadav I gave up reading it because of the emoji vomit sprinkled through it, which is a clear indication of it being LLM-generated. I’m sure I must have some form of dyslexia, as whenever emoji is sprinkled in text, I just find it 10Γ— harder to follow and comprehend.

I prefer to spend my time reading and replying to questions written by humans; not LLMs.

Snapey's avatar

GPS is not going to work indoors.

Consider reading something that is physically at the location, and is hard to copy, such as an NFC tag or a dynamic QR code that is displayed on a browser and changes every minute.

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