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OpenEduCat Documentation

Face Recognition Attendance

Face Recognition Attendance

Configure and use biometric face recognition for automated attendance tracking.

Time Required: 30-45 minutes (initial setup) Module: Attendance, Face Recognition (Enterprise) User Role: Attendance Manager, System Administrator

Table of Contents

  1. Overview
  2. Prerequisites
  3. System Setup
  4. Student Registration
  5. Mark Attendance
  6. Device Configuration
  7. Field Reference
  8. Troubleshooting

Overview

Face recognition attendance provides:

  • Contactless attendance marking
  • Automated check-in/check-out
  • Anti-proxy attendance measures
  • Integration with attendance sheets
  • Real-time synchronization

Note: This feature requires the Enterprise edition.

Prerequisites

Hardware Requirements

  • Camera device (webcam, IP camera, or dedicated device)
  • Minimum 720p resolution recommended
  • Adequate lighting in capture area
  • Stable network connection

Software Requirements

  • OpenEduCat Enterprise license
  • Face Recognition module installed
  • Python face recognition libraries configured
  • Camera drivers installed

Data Requirements

  • Students enrolled with photos
  • Attendance registers configured
  • Course and batch set up

System Setup

Step 1: Enable Face Recognition

  1. Go to OpenEduCat > Configuration > Settings
  2. Navigate to Attendance section
  3. Enable Face Recognition Attendance
  4. Click Save

Step 2: Configure Recognition Settings

SettingDescriptionRecommended
Recognition ToleranceMatch sensitivity0.6 (default)
Min ConfidenceMinimum match score70%
Anti-SpoofingLiveness detectionEnabled
Max Recognition TimeTimeout seconds10

Step 3: Set Up Recognition Model

  1. Go to OpenEduCat > Attendance > Face Recognition Settings
  2. Click Initialize Model
  3. Wait for model to load
  4. Test with sample image

Student Registration

Step 1: Capture Student Photos

For each student:

  1. Go to OpenEduCat > Students
  2. Open student record
  3. Go to Face Recognition tab
  4. Click Capture Face

Step 2: Photo Requirements

RequirementSpecification
LightingEven, front-facing
BackgroundPlain, contrasting
ExpressionNeutral, eyes open
AccessoriesRemove glasses, hats
Multiple AnglesCapture 3-5 images

Step 3: Verify Registration

  1. Click Test Recognition
  2. System attempts to identify student
  3. If successful, status shows “Registered”
  4. If failed, capture additional images

Bulk Registration

  1. Go to OpenEduCat > Attendance > Face Registration
  2. Select course and batch
  3. Click Start Bulk Capture
  4. Students approach camera in sequence
  5. System captures and registers each

Mark Attendance

Automatic Mode

  1. Go to OpenEduCat > Attendance > Face Recognition Kiosk
  2. Select course, batch, and session
  3. Click Start Recognition
  4. Students stand in front of camera
  5. System identifies and marks attendance

Recognition Process

Student Approaches → Face Detected → Identity Matched → Attendance Marked
                          ↓                  ↓
                    No Face Found      No Match Found
                          ↓                  ↓
                       Retry            Manual Entry

Manual Override

For unrecognized students:

  1. Click Manual Entry
  2. Select student from list
  3. Mark attendance status
  4. Optionally re-capture face

Device Configuration

Webcam Setup

  1. Go to OpenEduCat > Configuration > Devices
  2. Click Create
  3. Configure:
FieldDescription
NameDevice identifier
TypeWebcam
Device IDCamera index (0, 1, 2)
LocationPhysical location

IP Camera Setup

  1. Create new device
  2. Configure:
FieldDescription
NameCamera name
TypeIP Camera
URLRTSP or HTTP stream URL
UsernameCamera credentials
PasswordCamera password

Multiple Locations

Configure devices for different locations:

LocationDevicePurpose
Main GateIP Camera 1Entry check-in
Classroom AWebcamSession attendance
LibraryIP Camera 2Optional check-in

Field Reference

Recognition Settings

FieldTypeDescription
face_toleranceFloatMatch tolerance (0-1)
min_confidenceFloatMinimum score %
enable_antispoofingBooleanLiveness check
recognition_timeoutIntegerSeconds timeout

Student Face Data

FieldTypeDescription
face_encodingBinaryEncoded face data
face_imagesOne2manyCaptured images
face_registeredBooleanRegistration status
last_recognitionDatetimeLast recognized

Recognition Log

FieldTypeDescription
student_idMany2oneMatched student
confidenceFloatMatch score
timestampDatetimeRecognition time
device_idMany2oneSource device
imageBinaryCaptured image

Troubleshooting

Face not detected

Problem: Camera not detecting faces.

Solutions:

  1. Check camera is working (test with other app)
  2. Improve lighting conditions
  3. Ensure face is within frame
  4. Check camera resolution settings
  5. Verify face detection model loaded

Low recognition accuracy

Problem: Students not recognized correctly.

Solutions:

  1. Re-capture face images with better lighting
  2. Capture multiple angles
  3. Adjust tolerance setting (lower = stricter)
  4. Remove glasses/hats during capture
  5. Update face encodings periodically

Slow recognition

Problem: Takes too long to recognize.

Solutions:

  1. Reduce image resolution
  2. Limit students in recognition pool
  3. Check CPU/GPU performance
  4. Optimize recognition model
  5. Use batch-specific recognition

Anti-spoofing false positives

Problem: Real faces rejected as spoofs.

Solutions:

  1. Adjust anti-spoofing sensitivity
  2. Improve lighting conditions
  3. Reduce camera glare
  4. Temporarily disable for testing
  5. Update liveness detection model

Camera connection issues

Problem: Cannot connect to camera.

Solutions:

  1. Check camera is powered on
  2. Verify network connectivity (IP cameras)
  3. Check URL format and credentials
  4. Test camera with VLC or browser
  5. Check firewall settings