A Novel Face Identification Implementation for Class Attendance Monitoring

Face identification has become more significant and relevant in the recent years. It is widely used for security purposes in enterprises and state-owned business since it has many advantages and benefits compared to other state of the art security applications. Previous face identification implementations inherited many different approaches and algorithms in order to overcome the challenges of recognizing an individual from a variety of angles and heights, but none of them were completely successful. The main goal of this research is to demonstrate a novel face identification framework for an autonomous class attendance monitoring system implementing SIFT (Scale Invariant Feature Transform) algorithm. An image dataset, generated with the participation of 20 volunteers that were photographed from a variety of different angles and heights, was tested with the proposed system and achieved successful results in general with reasonable accuracy rates.