Facial Recognition Attendance System Using Deep Learning

Abstract

Abstract: Face authentication is one of the critical unsolved problems in computer science, a lot of time and energy is spent to invent a robust solution for it. Face authentication can play a significant role in security, Biometric verification, auto detection of criminals in a crowd, etc. The paper presents a unique model that authenticates the faces in the image, and securely shares the images. Firstly, the deep learning algorithm identifies all the faces in the image, extracts the invariant features of each face. The invariant feature of the face is studied to compute the emotions. The model captures the friends list of the user and securely shares the image to the known persons in the image.The main purpose of this project is to enhance the relationship between the student and the teaching staff. Due to pandemic every student are forced to attend the online classes, even though some students are tend to bunk the classes by attending those session by use some other else. In order to stop this we developed an windows application to take facial attendance and attending the class. The current old system has a lot of ambiguity that caused inaccurate and inefficient of attendance taking. Many problems arise when the authority is unable to enforce the regulation that exists in the old system. Thus, by means of technology, this project will resolve the flaws existed in the current system while bringing attendance taking to a whole new level by automating most of the tasks. The technology working behind will be the face recognition system. In the old system they use LDA. Although LDA is one of the most common data reduction techniques, it suffers from two main problems: The Small Sample Size (SSS) and linearity problems. We improvised the facial recognition using LBPH – (Local Binary Pattern Histograms) - Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision. This algorithm is so fast and reliable and the result prediction we have utilized a regression algorithm.