Face Recognition Attendance System — Automated student attendance using deep learning-based face detection and recognition with FastAPI backend

CNN-based Face Recognition Attendance System - Deep Learning attendance tracker with real-time facial recognition, automated CSV logging, and GUI interface using TensorFlow and OpenCV

A production-ready, full-stack attendance management system that uses deep learning-based face recognition to automate student attendance tracking with anti-spoofing protection, multi-camera support, and intelligent reporting.

A Geo-Location Based Attendance Tracking System integrated with AI-powered facial recognition using ResNet. The system automates student attendance by verifying real-time GPS location, detecting spoof-free face matches, ensuring secure, proxy-free authentication. Built using Python, Flask, SQL, Geopy, OpenCV, deep-learning–based face recognition.

A face recognition-based attendance system automates attendance tracking by using a camera to capture images of individuals as they enter a room. The system detects and recognizes faces using deep learning models like FaceNet. Once a face is identified, the system checks it against a database and automatically records attendance, logging the date a

ClassTrack is a full-stack face recognition–based attendance system that automates classroom attendance using a multi-stage deep learning pipeline. Built with FastAPI, MongoDB, and React, it performs face detection, embedding extraction, identity matching, and real-time attendance tracking, deployed via Docker on AWS EC2.

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An automated attendance management system using deep learning-based face recognition.

A Face Recognition-Based Attendance Management System that automates attendance tracking using deep learning and computer vision for accurate, secure, and efficient monitoring.

AI-powered web-based attendance system using face recognition and deep learning to automate attendance marking with real-time detection and timestamp logging.

Deep Learning based Face Recognition system that automate traditional attendance methods using the existing cameras(CCTV Camera) available over the organisation.

Face Recognition Attendance System This repository contains a facial recognition-based attendance system that automates attendance marking using OpenCV and deep learning. It captures and verifies faces in real-time, ensuring accurate and efficient attendance tracking. Ideal for classrooms & office.

The Face Recognition Based Attendance System is a smart solution that automates attendance using facial recognition technology. Built with Python, OpenCV, and deep learning (CNN via TensorFlow/Keras). Ask ChatGPT

A web-based face recognition attendance system using HTML, JavaScript, and face-api.js. It detects and identifies faces in real time via webcam, using deep learning models for detection and recognition, automating attendance marking efficiently and contactlessly.

Face Recognition–based Smart Attendance Management System built with Python, OpenCV, and Flask. Automates attendance logging using real-time facial recognition. Developed as part of SmartInternz Externship Program (Machine Learning & Deep Learning, 2023).

FRDATS is a Python-based face recognition and attendance system using Python and deep learning technologies designed to perform face recognition and digital attendance tracking using a webcam. It provides a convenient way to automate the attendance process by identifying individuals through facial recognition technology.

Automated classroom attendance system using deep learning (CNN, ResNet, HOG) for accurate face detection and recognition. Features real-time webcam integration, Excel-based storage, and a web interface for efficient attendance management.

A real-time attendance system using face recognition and geolocation verification. Built with Python, OpenCV, deep learning–based face embeddings, Flask, Firebase, and a React frontend to prevent proxy attendance and enable secure, automated check-ins.

Face Recognition Attendance System is a Python-based solution that automates attendance tracking using deep learning and facial recognition. It captures face images of students, extracts facial embeddings using the DeepFace library and Facenet, trains a neural network model for identity recognition, and logs attendance in real-time using a webcam.

Built a web-based attendance system using Python, Flask, and machine learning for real-time face recognition. Automated attendance marking, implemented faculty scheduling, session-wise attendance tracking, and secure login for students and faculty. Used OpenCV and deep-learning face encodings for accurate face matching and SQLite for data storage.

The Face Recognition Attendance System is a Python-based project developed using Tkinter and Convolutional Neural Networks (CNN). It automates attendance by detecting and recognizing faces in real-time, offering efficient attendance management. The system ensures accurate identification with high precision using a trained deep learning model.

This is my second year project . Developed a real-time Face Recognition Attendance System using Python, Flask, OpenCV, and deep-learning based face encodings. The system automates attendance marking, maintains session-wise records, and exports attendance data to Excel, enabling teachers to manage attendance via web or mobile browser

A cloud-based real-time multiface recognition system for automated attendance monitoring using CCTV feeds. Built with FastAPI, deep learning models, and database integration, this system detects, recognizes, and records multiple faces efficiently with high accuracy.

This project is a CCTV-based Face Recognition Attendance System that uses deep learning (dlib) and OpenCV to automate attendance tracking. It captures live video feed, detects known faces, and logs attendance with timestamps. Ideal for institutions, offices, and real-time surveillance applications.

INFRAWATCH is an AI-powered smart monitoring system that automates student attendance using CCTV cameras and computer vision. It leverages deep learning-based face recognition to identify students in real time and maintain accurate attendance records without manual intervention.

Built an automated attendance system in Python that uses deep-learning–based face recognition to detect, verify, and log student presence from live video. Integrates real-time processing with OpenCV and face_recognition, and stores records in SQLite/CSV with timestamped entries and daily reports.

Built an automated attendance system in Python that uses deep-learning–based face recognition to detect, verify, and log student presence from live video. Integrates real-time processing with OpenCV and face_recognition, and stores records in SQLite/CSV with timestamped entries and daily reports.

Face-X is a deep learning–based attendance system that uses real-time face recognition to automate presence tracking in classrooms or workplaces. Built using Python and Convolutional Neural Networks (CNNs), the system detects and recognizes faces from a webcam feed, matches them with stored records, and logs attendance instantly—eliminating manual

Smart Attendance System is an AI-based application that automates the process of marking attendance using facial recognition. By leveraging computer vision and deep learning, the system detects and recognizes faces in real time from both images and live video feeds. It replaces traditional manual or biometric attendance methods.