{"repo":"shreyamalogi/Biometric-Attendance-Engine","free":true,"listed":false,"github":"https://github.com/shreyamalogi/Biometric-Attendance-Engine","clone":"git clone https://github.com/shreyamalogi/Biometric-Attendance-Engine.git","description":"Real-time face recognition system using HOG encodings and Dlib landmarks. Features a high-speed Flask/OpenCV pipeline for live video processing and automated SQL database logging","language":"HTML","stars":18,"topics":["flask","sqlite","bootstrap","opencv-python","html","css","sqlalchemy","jinja2","face-recognition","hog-features"],"license":"MIT","category":"databases-storage","readme_excerpt":"📸 Biometric Attendance Engine: Real-Time Identity Verification 🏆 Award-Winning Innovation: Selected as a Finalist among 100+ competing teams (Team Mavericks) 📖 The \"Problem-to-Solution\" Narrative Manual attendance tracking is a high-friction administrative task prone to data latency and \"proxy\" errors. As a core developer for Team Mavericks , I collaborated on building an AI-powered ecosystem designed to make identity verification seamless and totally automated. Our mission was to move facial recognition out of a static script and into a functional, secure web application capable of handling diverse input streams in real-time. --- 🏗️ System Architecture: Three-Mode Versatility To ensure the system was practical for real-world institutional environments, we engineered 3 specialized processing modes : Live Feed : Real-time identification via active webcam streams for immediate classroom logging. Static Image : Batch-processing of photographs for post-event verification and archival. Video Playback : Asynchronous analysis of recorded footage to register attendance from pre-captured video files. --- 🛠️ The Technical Stack Core Developer & Backend Integration: Shreya Malogi I contributed to the full-cycle development of this project, with primary ownership of the Backend Architecture and Data Integration Layer : AI & Computer Vision Methodology : Utilized HOG (Histogram of Oriented Gradients) for robust facial feature encoding, ensuring the model identifies structural shapes ","default_branch":null,"files":null,"tree":[],"storefront":"/r/shreyamalogi","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/shreyamalogi/Biometric-Attendance-Engine/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}