Facial Recognition and Automatic Attendance System

Abstract

Human face detection and recognition have significantly been a topic of research as is now used in many areas of applications such as social media, surveillance, defense and national security, etc. In this project both face detection and recognition techniques have been worked on, which includes Viola-Jones algorithm, Feature Extraction, Machine Learning algorithms like error-correcting output codes (ECOC), etc. where an unidentified test image of a person has been recognized by extracting its features and passing it to a classifier which has been trained by the features of the training images stored in the database as well as gives information regarding the person recognized. These techniques work well under robust conditions like the complex background, different face positions. These algorithms give different rates of accuracy under different conditions.