Smart Attendance System Usign CNN
This is an incremental improvement for attendance systems in colleges or offices, addressing flaws in existing methods to make the process more efficient and less time-consuming.
The paper tackles the problem of automating attendance systems by proposing a smart system using face detection and recognition with Convolutional Neural Networks (CNN) to overcome issues like lighting and posture sensitivity found in conventional methods, resulting in automatic attendance recording and storage in databases like MongoDB and Excel sheets.
The research on the attendance system has been going for a very long time, numerous arrangements have been proposed in the last decade to make this system efficient and less time consuming, but all those systems have several flaws. In this paper, we are introducing a smart and efficient system for attendance using face detection and face recognition. This system can be used to take attendance in colleges or offices using real-time face recognition with the help of the Convolution Neural Network(CNN). The conventional methods like Eigenfaces and Fisher faces are sensitive to lighting, noise, posture, obstruction, illumination etc. Hence, we have used CNN to recognize the face and overcome such difficulties. The attendance records will be updated automatically and stored in an excel sheet as well as in a database. We have used MongoDB as a backend database for attendance records.