{"id":27278961,"url":"https://github.com/pradeep-r04/attendiq","last_synced_at":"2026-04-02T03:11:18.130Z","repository":{"id":287336289,"uuid":"964397464","full_name":"pradeep-r04/attendiq","owner":"pradeep-r04","description":"AttendIQ is a Face Recognition Attendance System designed to automate and streamline the attendance process with precision and ease. By leveraging real-time face detection and recognition technology, AttendIQ eliminates the need for manual roll calls or ID-based check-ins.  The system captures facial data during a quick registration process .","archived":false,"fork":false,"pushed_at":"2025-04-11T07:06:07.000Z","size":3971,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-09T05:35:14.780Z","etag":null,"topics":["csv","cv2","kneighborsclassifier","numpy","os","pandas","pickle","python","scikit-learn","streamlit","time"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pradeep-r04.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-11T06:39:07.000Z","updated_at":"2025-04-11T07:31:25.000Z","dependencies_parsed_at":"2025-04-11T08:57:44.058Z","dependency_job_id":"ac3a203c-73d1-43f1-b089-b140d58c7d0a","html_url":"https://github.com/pradeep-r04/attendiq","commit_stats":null,"previous_names":["pradeep-r04/attendiq"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pradeep-r04/attendiq","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradeep-r04%2Fattendiq","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradeep-r04%2Fattendiq/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradeep-r04%2Fattendiq/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradeep-r04%2Fattendiq/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pradeep-r04","download_url":"https://codeload.github.com/pradeep-r04/attendiq/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pradeep-r04%2Fattendiq/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31294920,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-02T01:43:37.129Z","status":"online","status_checked_at":"2026-04-02T02:00:08.535Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["csv","cv2","kneighborsclassifier","numpy","os","pandas","pickle","python","scikit-learn","streamlit","time"],"created_at":"2025-04-11T17:46:19.580Z","updated_at":"2026-04-02T03:11:18.108Z","avatar_url":"https://github.com/pradeep-r04.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"#  🎯 AttendIQ – Face Recognition Attendance System\nAttendIQ is a Face Recognition Attendance System designed to automate and streamline the attendance process with precision and ease. By leveraging real-time face detection and recognition technology, AttendIQ eliminates the need for manual roll calls or ID-based check-ins.  The system captures facial data during a quick registration process .\n\n\n---\n\n## 🚀 Features\n\n- 🔍 Real-time Face Detection \u0026 Recognition\n- 🧑‍💼 User Registration with ID \u0026 Name\n- 📸 Captures only 5 face samples per user\n- ✅ Attendance marked only on pressing the `o` key\n- 🗂 Attendance stored in timestamped CSV files\n- 🗣 Voice feedback for successful attendance\n- 📊 Streamlit dashboard to view attendance data\n- 📁 Modular structure with separate files for training, recognition, and interface\n\n---\n\n## 🧰 Technologies Used\n\n- Python 3.x  \n- OpenCV  \n- NumPy  \n- Scikit-learn (KNN)  \n- Streamlit  \n- win32com (for text-to-speech on Windows)  \n- CSV, Pickle (for data storage)  \n\n---\n\n## 📁 Project Structure\nface_recog/ ├── data/ │ ├── haarcascade_frontalface_default.xml │ ├── names.pkl │ └── faces_data.pkl ├── Attendance/ │ └── Attendance_dd-mm-yyyy.csv ├──  ├── main.py # User registration and sample capture ├── test.py # Face recognition \u0026 attendance logging ├── app.py  # Streamlit interface └── README.md\n\n\n---\n\n## 🧑‍🎓 How It Works\n\n1. **Register User (main.py)**  \n   - Input user ID and Name\n   - System captures 5 face samples\n   - Saves data into `faces_data.pkl` and `names.pkl`\n\n2. **Recognize \u0026 Mark Attendance (test.py)**  \n   - Launches webcam feed\n   - Detects and recognizes registered faces\n   - Press `o` key to log attendance into a dated CSV file\n   - Press `q` to exit\n\n3. **Streamlit Dashboard (app.py)**  \n   - Run the UI with `streamlit run app.py`\n   - Register users, capture faces, and view attendance data in a user-friendly interface\n\n---\n\n## ▶️ Getting Started\n\n\n## 📌 Notes  \nEnsure your webcam is working properly.  \nPress o to mark attendance after face is recognized.  \nEach user is registered with exactly 5 face samples.  \nAttendance records are saved in the Attendance/ folder, labeled by date.  \n\n## 🙌 Acknowledgements  \nOpenCV – for real-time face detection  \nscikit-learn – for implementing KNN classification  \nStreamlit – for making the interface interactive  \nMicrosoft Speech API – for text-to-speech feature \n\n## 📜 License\nThis project is for educational and personal use only.  \n\n## 💡 Future Enhancements  \nDatabase integration (e.g., SQLite or Firebase)  \nEmail/SMS notification support  \nAdmin login for secured access  \nAttendance analytics dashboard  \n\n## Connect with me:\n| Name    | Email              | LinkedIn                                      | GitHub                      | Instagram                     |\n|---------|--------------------|-----------------------------------------------|-----------------------------|-------------------------------|\n| Pradeep | [Email](pradeep.singh04r@gmail.com)  | [LinkedIn](https://linkedin.com/in/pradeep-singh4) | [GitHub](https://github.com/pradeep-r04) | [Instagram](https://instagram.com/whypradeeep) |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpradeep-r04%2Fattendiq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpradeep-r04%2Fattendiq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpradeep-r04%2Fattendiq/lists"}