Face Recognition based Attendance System

Abstract

Human face is one of the natural traits and crucial part of human body that can uniquely identify an individual. In the current old system the roll numbers are called out by the teachers and their presence or absence is marked accordingly which is time consuming and has a lot of ambiguity that caused inaccuracy and inefficiency of attendance marking. The productive time of the class can be utilized very efficiently by implementing automated attendance system. The main purpose of this project is to build a face recognition-based attendance monitoring system for any educational institution or organization where attendance marking is the demanding task. It enhances and upgrades the current attendance system into more efficient and effective as compared to before. This attendance system which uses HaarCascade a machine learning Object Detection Algorithm used to identify faces in an image or a real time video, Local Binary Pattern Histogram (LBPH) a face recognizer algorithm used to extract features and compare by using python programming and OpenCV libraries saves time and efficiently identifies and eliminates the chances of proxy attendance. This model integrates a camera that captures an input image and training database is created by training the system with the faces of the authorized students.