Implementing Machine Learning for Face Recognition based Attendance Monitoring System

It is well known that marking attendance of the students is an obligatory part in academia. Conventional method of marking the attendance is being followed by various institutions and Universities with lots of manual interventions. To reduce time consumption and human effort, the use of an automatic process of marking attendance based on image processing can be enforced. Authors have proposed a smart attendance monitoring system through face detection and recognition techniques based on their facial features. A set of images of the students are previously fed to the system against which the live images of the students are compared and attendance would be recorded based on facial characteristics. The proposed approach uses CNN algorithm for training the images and LBPH visual descriptor for image classification. This model will be capable of providing higher degree of accuracy compared to already existing literature work. Authors have compared their experimental results with the existing approaches and found satisfactory.