Contact person: Business Client

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Location: Jeddah, Saudi Arabia

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"Professional Project Brief – Revised for School Entrance Deployment

Subject: Python Developer Needed: Automated Attendance System at School Entrance Using Facial Recognition (YuNet + InsightFace, CPU-Only, 900+ Students)

I’m deploying a fully automated attendance system at the main entrance of a school (in the courtyard, near the gate). The goal is to log every student’s arrival time as they enter the campus — without any manual input — and automatically classify them as:
• Present (on time)
• Late (after official start time)
• Absent (not detected by cutoff time)

The system must run continuously during morning hours on a standard Windows 11 PC (CPU-only), using a single PoE camera installed at the entrance.
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Non-Negotiable Technical Requirements
1. Open & Verifiable Models Only
• Face detection: Must use the official OpenCV YuNet model ([login to view URL] from OpenCV Zoo).
• Face recognition: Must use InsightFace with ArcFace (e.g., buffalo_l).
• No private, undocumented, or black-box models (e.g., [login to view URL]).

2. Precomputed Embeddings for Real-Time Scalability
• Student identities stored as numeric embeddings (.npy files), not raw images.
• Live recognition via instant cosine similarity against precomputed database.
• Must handle 900+ students in real time on CPU-only hardware (no GPU).

3. Accuracy in Real-World Entrance Conditions
• Target: ≥95% true-positive rate under typical outdoor/semi-outdoor school entrance lighting (morning sun, partial shade, etc.).
• Robust to common challenges: hats, partial occlusion, varying distances.
• Configurable similarity threshold with clear inline documentation.

4. Transparent, Maintainable Code
• Pure Python source code only —no .exe, [login to view URL], closed SDKs, or obfuscated binaries.
• Fully modular structure:
• [login to view URL] (detection + recognition)
• [login to view URL] (on-time/late/absent logic based on time windows)
• [login to view URL] (PostgreSQL or MySQL)
• [login to view URL] (PoE camera integration via OpenCV)
• [login to view URL] (daily Excel + PDF exports)
• Every function includes clear inline comments (purpose, inputs, outputs, parameters).

5. Automated Daily Workflow
• System starts automatically at school opening time (e.g., 7:00 AM).
• Continuously processes video stream, detects faces, and logs first valid detection per student per day.
• At cutoff time (e.g., 7:30 AM), marks non-detected students as Absent.
• Generates daily attendance report (Excel + PDF) by class/date.
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Deliverables
• Bill of materials: Recommended affordable PoE camera suitable for outdoor/semi-outdoor school entrance (with weather protection if needed).
• Camera placement & network setup guide: Optimal height, angle, and lighting considerations for reliable detection.
• Complete source code with [login to view URL], modular structure, and enrollment script (to register students from photos → embeddings).
• Working demo video: Simulating real-world entrance scenario with multiple students walking in.
• Step-by-step deployment guide for IT staff (Windows 11 setup, camera connection, service auto-start).
• Database schema + sample queries + optional lightweight dashboard or REST API for attendance lookup.
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Final Validation
I will conduct a real-world test at the school entrance during morning arrival. The system must:
• Detect and recognize students as they walk through the gate
• Log accurate arrival timestamps
• Correctly classify each as Present, Late, or Absent based on predefined time windows
• Run stably for 3+ hours on a standard Windows 11 PC (CPU-only)

Note: I will not accept partial code, undocumented models, manual capture steps, or closed binaries.
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If you can deliver a transparent, open-source, production-ready system that meets these requirements, please reply with:
1. Your experience with InsightFace + YuNet in real-world deployments
2. Your approach to handle 900+ students under variable outdoor lighting
3. Estimated timeline & cost
I’m looking for a developer who values verifiability, maintainability, and ethical AI — not just a quick demo." (client-provided description)