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# Face Recognition Attendance System With Engagement Detection
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**Tier:** 3-Advanced
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Attendance tracking in classrooms is time-consuming and prone to human error. This system automates this process using real-time face recognition, detecting not only who is present but also whether students are engaged — flagging behaviors like phone usage and sleeping with snapshot evidence.
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This project challenges the developer to combine computer vision, a real-time web interface, and session-based reporting into a single cohesive system.
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## User Stories
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* User can start a live camera session that detects and recognizes faces in real time
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* User can register student faces into the system before a session begins
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* User views a live dashboard showing attendance status for each recognized student
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* User can see flagged events (phone usage, sleeping) with timestamps and snapshot images
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* User can export attendance records for a session as a CSV file
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* Attendance is automatically marked absent when a flagged behavior is detected
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## Bonus features
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* User can set a minimum attendance threshold and receive an alert when a student falls below it
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* User can view session history with per-student engagement scores over time
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* User can support multiple classrooms with separate student rosters
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* Admin can add or remove students from the system without restarting the server
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* System sends an email or SMS notification to flagged students after a session
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## Useful links and resources
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* [OpenCV Haar Cascade face detection](https://docs.opencv.org/4.x/db/d28/tutorial_cascade_classifier.html)
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* [LBPH face recognizer](https://docs.opencv.org/4.x/df/d25/classcv_1_1face_1_1LBPHFaceRecognizer.html)
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* [YOLOv8 object detection](https://docs.ultralytics.com/)
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* [Flask-SocketIO for real-time communication](https://flask-socketio.readthedocs.io/)
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* [Head pose estimation with OpenCV](https://learnopencv.com/head-pose-estimation-using-opencv-and-dlib/)
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## Example projects
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* [Face Recognition Attendance System with Engagement Detection](https://github.com/nhsync/Face-Recognition-Attendance-System-With-Engagement-Detection)
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# Poker Night
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**Tier:** 3-Advanced
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Multiplayer card games require careful state management, real-time
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communication, and fair game logic running server-side. Poker Night is
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a fully browser-based Texas Hold'em game supporting 2 to 8 concurrent
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players with no database dependency — all game state lives in memory
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and is synchronized via WebSockets.
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This project challenges the developer to implement a complete poker hand
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evaluator, a turn-based state machine, and a real-time multiplayer
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experience from scratch.
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## User Stories
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* User can create a game room and share a room code with friends
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* User can join an existing room using a room code
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* User sees their own hole cards and the shared community cards on the table
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* User can perform all standard poker actions: fold, check, call, raise
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* User views a live list of all players, their chip counts, and current bet
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* User is shown the winner and their hand rank at the end of each round
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* Game enforces turn order and rejects out-of-turn actions
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* Game supports 2 to 8 players in a single room
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## Bonus features
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* User can set a starting chip count and blind structure when creating a room
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* Spectator mode allows users to watch a game without playing
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* User sees an animated card deal and chip movement for each action
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* Game handles disconnections gracefully by auto-folding the disconnected player
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* User can view a hand history log for the current session
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* Leaderboard tracks chip counts across multiple rounds in a session
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## Useful links and resources
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* [Texas Hold'em rules](https://www.pokerstars.com/poker/games/texas-holdem/)
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* [Poker hand rankings](https://en.wikipedia.org/wiki/List_of_poker_hands)
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* [Socket.IO documentation](https://socket.io/docs/v4/)
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* [Building a card game state machine](https://statecharts.dev/)
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* [Evaluating poker hands programmatically](https://medium.com/@geekgirl907/poker-hand-evaluator-54a00c8e3b3d)
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## Example projects
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* [Poker Night](https://github.com/nhsync/poker-night)
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