Attendance System using face recognition. Fully automated, UI operated. The model was trained using Keras Sequential layers and Softmax function at the output layer. The data is maintained in MongoDB and CSV at the backend. UI is created using Tkinter

Face Recognition on NIST FRVT Top Ranked, Face Detection, Face Matching, Face Analysis, Face Sentiment, Face Alignment, Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution

Face Recognition on NIST FRVT Top Ranked, Face Detection, Face Matching, Face Analysis, Face Sentiment, Face Alignment, Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution on Linux

Face Recognition on NIST FRVT Top Ranked, Face Detection, Face Matching, Face Analysis, Face Sentiment, Face Alignment, Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution on Windows

Smart Face Attendance System

NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof) Flutter SDK Demo ☑️ Face Recognition ☑️ Face Matching ☑️ Face Liveness Detection ☑️ Face Identification (1:N Face Search) ☑️ Face Pose Estimation

Attention is a face recognition attendance system that automates the process of tracking attendance using facial recognition technology.

Some project with python

Face Attendance Project with OpenCV

Face Attendence project in which the Attendence is Automatically marked after Scanned the images of Students

This ReactJS application manages a student attendance system that integrates with the companion iOS mobile app

The face attendance system uses facial recognition to automatically track attendance without the need for manual input.