Class Management System

Designed and implemented a sophisticated face recognition system to automate the process

of recording students attendance.

Developed the system using advanced computer vision techniques and machine learning

algorithm to accurately identify and authenticate students based on facial features

Conducted comprehensive data collection from students using a Python script that

automates the process and reduces time spent by 90%.

implemented data preprocessing techniques such as cleaning, transformation, and features

extraction to prepare raw datasets for a analysis and model development

Tools and Technologies: OpenCV, Tensorflow, Python, MTCNN, SVM, Raspberry Pi,

Camera module

Collaborated with cross-functional teams including software engineers and educators to

gather requirements, prioritize, and deliver a user-friendly solution.

Achieved a significant reduction in manual attendance tracking efforts and improved

accuracy by over 95%, leading to enhance efficiency and accountability within the

educational institution