Neural Network based Biometric Attendance System

In the modern world, education system has reached a new destination due to the introduction of concept called “smart classroom”. However, when we are speaking about any classroom the attendance system still remains primitive. The traditional attendance system where the teacher/lecturer calls the name of students to mark their attendance in an attendance register is a manual method which is found to be not suitable for a smart class due to a list of disadvantages. The automatic attendance management will replace the manual method. This dynamic attendance management system will consider the physiological features of the human beings for uniquely identifying them. Hence we are using a biometric based attendance system. There are many biometric processes, among which face recognition is the best method. In the proposed project, we are going to describe the attendance without human interference. In this method a camera, fixed within the classroom will capture the image, the faces are detected and then they are compared with the faces in the database and finally the attendance is marked. It also proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces. Freely available machine learning and deep learning tools like dlib, Keras are used for making the face recognition faster and accurate one. This makes the system suitable in a real life scenario.