Face Recognition Attendance System
Impact98% recognition accuracy
Accuracy98%
Processing<500ms
Employees2000+
Overview
Developed a face recognition-based attendance system for a corporate client to automate employee check-ins. The system uses deep learning models for face detection and recognition, handling variations in lighting, angles, and accessories.
The Challenge
Traditional attendance systems were time-consuming and prone to buddy punching. The client needed an automated, fraud-proof solution that works reliably across different conditions.
The Solution
Built a multi-stage CV pipeline using MTCNN for face detection, FaceNet for embeddings, and custom classifiers. Implemented anti-spoofing measures and real-time processing for seamless check-ins.
Key Results
98% face recognition accuracy
Real-time processing under 500ms
Deployed for 2000+ employees
Tech Stack
PythonOpenCVTensorFlowMTCNNFaceNetFastAPIPostgreSQLRedis