Wednesday, December 10, 2025

SRS - Face Recognition Attendance System feature list into a structured table (like for a proposal or feature matrix) or into a developer backlog (with user stories and priorities)

 



  • Scope, stakeholders, and system overview

  • Detailed functional and non-functional requirements with acceptance criteria

  • DB schema, sample APIs, and example JSON payloads

  • ML model lifecycle, liveness, and accuracy targets

  • Privacy, security, and compliance guidance (consent/retention)

  • Deployment options (edge/cloud/hybrid), hardware recommendations

  • Testing plan, monitoring, migration strategy, and developer handoff checklist

Next steps I can do for you (pick any):

  • Generate an OpenAPI (Swagger) skeleton for the APIs.

  • Produce a prioritized backlog of user stories with acceptance tests.

  • Export the SRS to PDF or a developer-ready README.

  • Create a basic repo layout and CI/CD YAML templates.

.

? 1. Core Functional Features

1.1. User Enrollment

  • Register users (students, employees, staff) with personal and identification data.

  • Capture and store multiple facial images or a short video for enrollment.

  • Perform image quality checks — lighting, clarity, face angle, blur detection.

  • Generate and store facial embeddings (AI vectors) instead of raw images.

  • Handle re-enrollment or updating facial data when needed.

  • Collect consent confirmation during enrollment (privacy compliance).

1.2. Face Recognition Attendance

  • Real-time facial recognition from webcam, CCTV, or mobile camera.

  • Automatic detection and identification of users.

  • Record attendance with status: PRESENT / UNVERIFIED / ABSENT.

  • Configurable match threshold (default: 0.75 similarity).

  • Automatic duplicate prevention within a time frame (to stop re-check-ins).

  • Attendance capture through web, mobile app, or dedicated terminal.

  • Offline mode: Edge devices can capture and sync later when online.

1.3. Liveness Detection

  • Detects whether the face belongs to a live person (not a photo/video).

  • Supports passive liveness (texture analysis) and active liveness (blink, smile, head movement).

  • Configurable liveness confidence threshold (default: 0.7).

  • Attendance is rejected if liveness fails (to prevent spoofing).

1.4. Attendance Management

  • Auto-mark attendance once a valid face match is found.

  • Manual correction/review by admin for unverified records.

  • Supports configurable working hours, grace periods, break times.

  • Displays daily, weekly, monthly summaries per user, department, or branch.

  • Handle shift-wise or class-wise attendance policies.

  • Geolocation tagging or device ID logging for security.

?￢ﾀﾍ? 2. Administration & Management Features

2.1. Admin Dashboard

  • Manage users, attendance, and devices through a responsive web interface.

  • View real-time attendance status (who is present/absent).

  • Approve or reject unverified attendance records.

  • Configure match thresholds, retention policies, and working schedules.

  • Manage roles and permissions (RBAC): SuperAdmin, Admin, Manager, Verifier, Auditor.

  • Access audit logs for all major actions.

  • Configure email/SMS notifications for anomalies or daily summaries.

2.2. Device Management

  • Register and monitor capture terminals (camera devices, kiosks, mobile apps).

  • Track device location, IP, and last-seen timestamp.

  • Manage device configurations remotely.

  • Enable/disable specific devices for maintenance or policy violations.

2.3. Reporting & Analytics

  • Generate attendance reports by:

    • Date range

    • User or department

    • Class/shift

    • Device/location

  • Export reports in CSV, Excel (XLSX), or PDF.

  • Schedule automated report generation (daily, weekly, monthly).

  • Interactive charts and dashboards (attendance trends, peak times).

  • Detect anomalies (suspicious or repeated attendance patterns).

2.4. Integration & API

  • RESTful APIs for user sync, attendance posting, and report fetching.

  • Webhook support (for enrollment completion, attendance verified, etc.).

  • Integration with HR, ERP, or Student Management Systems (via API or CSV).

  • Single Sign-On (SSO) via OAuth2 / SAML.

  • LDAP integration for enterprise environments.

  • OpenAPI/Swagger documentation for all API endpoints.

? 3. AI & Recognition Engine Features

3.1. Face Recognition Engine

  • Detects faces, extracts embeddings, matches against database.

  • Configurable inference modes:

    • Edge Mode: On-device inference (ONNX/TensorRT).

    • Cloud Mode: Centralized GPU inference.

    • Hybrid Mode: Local detection + cloud matching.

  • Maintain multiple model versions (for retraining, A/B testing).

  • Accuracy goals:

    • False Accept Rate (FAR) ≤ 0.1%

    • False Reject Rate (FRR) ≤ 3%

3.2. Model Lifecycle Management

  • Track deployed model versions and accuracy metrics.

  • Retrain or fine-tune models using new enrollment data (if consented).

  • Automatic fallback to previous stable model if new version underperforms.

  • Evaluate models on controlled datasets with ROC/FAR/FRR metrics.

? 4. Security & Compliance Features

  • Data encryption at rest (AES-256) and in transit (TLS 1.2+).

  • Store face embeddings only, not raw face images where possible.

  • Role-based access and least-privilege principles.

  • Two-factor authentication (2FA/MFA) for admin logins.

  • Audit trails for all critical actions (who, what, when, where).

  • Configurable data retention (e.g., purge raw images after 30 days).

  • GDPR / Privacy law compliance: user consent, data export, right to delete.

  • IP whitelisting and request signing for devices.

  • Automatic detection of suspicious activities (e.g., repeated spoof attempts).

⚙️ 5. System & Technical Features

5.1. Architecture

  • Modular microservice architecture.

  • REST API backend + modern frontend (React, Vue, or Angular).

  • Database: PostgreSQL / MySQL (configurable).

  • Optional message queue (RabbitMQ/Kafka) for async processing.

  • Containerized (Docker/Kubernetes ready).

  • CI/CD pipeline with automated testing and deployment.

5.2. Performance

  • Attendance recognition latency:

    • Edge: < 500ms

    • Cloud: < 250ms

  • Scalable to 1,000+ concurrent device connections.

  • Load balancing and horizontal scaling supported.

5.3. Reliability & Monitoring

  • Target uptime: 99.5%

  • Auto backup for database and storage.

  • Health checks and monitoring dashboards.

  • Alerts for failed recognition spikes or system anomalies.

5.4. Usability

  • Simple, guided UI for enrollment and attendance capture.

  • Real-time visual feedback (✅ recognized / ❌ unverified).

  • Accessible on desktops, mobiles, and kiosks.

  • Multilingual interface support (optional).

  • Dark/light themes for user comfort.

? 6. Optional / Phase-2 Features

  • Group attendance capture (detect multiple faces at once).

  • Visitor mode (temporary user via QR + selfie).

  • Emotion detection / mood analytics (optional & privacy-reviewed).

  • Voice + Face fusion for high-security zones.

  • Geo-fencing: mark attendance only at approved locations.

  • Offline synchronization: store records locally and sync later.

✅ In Summary — Major Functional Blocks:

Category

Key Features

User Enrollment

Face capture, quality check, consent

Attendance Capture

Face recognition, liveness, time logging

Admin & Dashboard

Manage users, devices, policies, reports

Reports

Analytics, trends, exports

API Integrations

REST API, SSO, LDAP, webhooks

Security & Privacy

Encryption, RBAC, logs, compliance

ML Engine

Detection, embedding, matching, model lifecycle

Deployment & Monitoring

Scalable backend, CI/CD, alerts


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