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Face Recognition Attendance System

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Add Face Images

  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough

3. Run the System

python main.py

4. Start Recognition

  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit

📁 Project Structure

├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features

  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring

🎯 Perfect For

  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing

Ready to use - just add face images and run! 🎉

About

Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.

Topics

attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Face Recognition Attendance System
🚀 Quick Start
1. Install Dependencies
pip install -r requirements.txt
2. Add Face Images
  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough
3. Run the System
python main.py
4. Start Recognition
  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit
📁 Project Structure
├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features
  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring
🎯 Perfect For
  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing
Ready to use - just add face images and run! 🎉
About
Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.
Topics
attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter
Resources
Readme
Activity
Stars
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0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Face Recognition Attendance System
🚀 Quick Start
1. Install Dependencies
pip install -r requirements.txt
2. Add Face Images
  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough
3. Run the System
python main.py
4. Start Recognition
  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit
📁 Project Structure
├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features
  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring
🎯 Perfect For
  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing
Ready to use - just add face images and run! 🎉
About
Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.
Topics
attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Face Recognition Attendance System
🚀 Quick Start
1. Install Dependencies
pip install -r requirements.txt
2. Add Face Images
  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough
3. Run the System
python main.py
4. Start Recognition
  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit
📁 Project Structure
├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features
  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring
🎯 Perfect For
  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing
Ready to use - just add face images and run! 🎉
About
Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.
Topics
attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Face Recognition Attendance System
🚀 Quick Start
1. Install Dependencies
pip install -r requirements.txt
2. Add Face Images
  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough
3. Run the System
python main.py
4. Start Recognition
  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit
📁 Project Structure
├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features
  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring
🎯 Perfect For
  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing
Ready to use - just add face images and run! 🎉
About
Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.
Topics
attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Face Recognition Attendance System
🚀 Quick Start
1. Install Dependencies
pip install -r requirements.txt
2. Add Face Images
  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough
3. Run the System
python main.py
4. Start Recognition
  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit
📁 Project Structure
├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features
  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring
🎯 Perfect For
  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing
Ready to use - just add face images and run! 🎉
About
Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.
Topics
attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Face Recognition Attendance System
🚀 Quick Start
1. Install Dependencies
pip install -r requirements.txt
2. Add Face Images
  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough
3. Run the System
python main.py
4. Start Recognition
  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit
📁 Project Structure
├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features
  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring
🎯 Perfect For
  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing
Ready to use - just add face images and run! 🎉
About
Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.
Topics
attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Face Recognition Attendance System
🚀 Quick Start
1. Install Dependencies
pip install -r requirements.txt
2. Add Face Images
  • Put face images in the known_faces folder
  • Name format: PersonName.jpg (e.g., john.jpg, mary.png)
  • One clear photo per person is enough
3. Run the System
python main.py
4. Start Recognition
  1. Click "Start Recognition" button
  2. Camera window opens with confidence percentages
  3. System recognizes faces and logs attendance to attendance.xlsx
  4. Press 'q' or click "Stop" to quit
📁 Project Structure
├── main.py # Main application
├── face_recognition_engine.py # Face recognition logic
├── camera_manager.py # Camera handling
├── attendance_logger.py # Excel logging
├── known_faces_manager.py # Face image processing
├── requirements.txt # Dependencies
├── attendance.xlsx # Attendance records
└── known_faces/ # Face images folder
└── PersonName.jpg # Add your face images here

✨ Features
  • High-FPS Recognition with confidence percentages
  • Screen Image Optimized - Works with Google Images/Internet photos
  • Color-coded Confidence - Green (>80%), Yellow (60-80%), Orange (40-60%), Red (<40%)
  • Excel Attendance Logging with duplicate prevention
  • Single Image Training - One photo per person handles all expressions
  • Real-time FPS Display for performance monitoring
🎯 Perfect For
  • Office attendance systems
  • School/university check-ins
  • Event registration
  • Security applications
  • Google Images recognition testing
Ready to use - just add face images and run! 🎉
About
Real-time face recognition attendance system with GUI - logs attendance to Excel, supports high-FPS detection with confidence scoring, and works with webcam or internet images.
Topics
attendance-systemattendance-trackingcomputer-visionexcelface-detectionface-recognitionfacial-detectionguiloggingopencvpythontkinter
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