Online Classroom Attendance System With Multiple Face Recognition

In this digital world automation is most effective and essential in all aspects. Getting and maintaining classroom attendance for every hour in a day at school or college by calling names and marking on book is very hectic and time-consuming, there are some chances of proxy attendance. There are many automated human identification techniques such as biometrics, RFID, voice recognition still those techniques some issues systems are vulnerable to proxies. But we are implementing the multiple face recognition technique to get all candidate in single or multiple frames to get attendance conformation. The algorithms are implemented using a series of signal processing methods including Harr-cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. Faces are detected and recognized from live streaming video of the classroom. Attendance will be updated student details with date and time to online database