Real-Time Facial Recognition Based Smart Attendance Management System Using Haar Cascading and LBPH Algorithm

Taking attendance of the students present in the classes is a regular part of an institution's day-to-day operations everywhere in the world. Traditionally, attendance is taken by a roll call or by inputting data into the computer, which takes a long time and can result in false attendance. To alleviate the time and errors associated with the traditional process, this research paper proposes a solution using real-time facial recognition. The majority of a student's daily attendance can be managed with real-time facial recognition. Face recognition refers to the process of recognizing a student's face to take attendance using face biometric data. There are several research papers that only look at student recognition rates. This study focuses on a facial recognition-based attendance system with a high confidence level and a low false-positive rate. This study demonstrates the capability of facial identification by combining the Local Binary Pattern Histogram (LBPH) algorithm and the Haar cascading algorithms because of their robustness against monotonic grayscale transformations. This provides a facial map of the individual, which aids in the post-image processing of the individual image obtained during attendance. This system can identify students even if they have facial hair or are wearing spectacles. This method's efficiency was higher when compared to traditional techniques; however, it did have several disadvantages that may be readily rectified by enhancing the environment and applying deep learning via machine computing using artificial intelligence.