Face Recognition Attendance System

Abstract: Face recognition attendance systems have gained popularity in recent years as they offer an efficient and secure method of monitoring employee attendance. This system can help in reducing errors and increasing efficiency as compared to traditional manual methods of taking attendance. The system makes use of computer vision technology to detect and recognize the faces of employees and record their attendance automatically Our system consists of two main parts: face detection and recognition. The first step is to detect faces in an image using a pre-trained deep learning model. The model used for this task is the Single Shot Detector (SSD) model, which is trained on the COCO dataset. The model detects faces in an image and draws bounding boxes around them. The second step is face recognition, which involves comparing the detected faces with a pre-existing database of employee faces. For this task, the system uses the FaceNet model, which is trained on a large dataset of faces and can generate a high-dimensional feature vector for each face. The feature vectors are then compared using the cosine similarity measure to determine if a given face matches a face in the database. Our system also includes a user interface that allows administrators to view attendance records and add or remove student details from the database. The interface is built using the PyQt5 library and provides an easy-to- use graphical user interface. Our system has several advantages over traditional attendance systems. It eliminates the need for manual entry, reducing the chances of errors and fraud. It also saves time by automating the attendance process and reduces the workload of administrative staff. Furthermore, it provides enhanced security by preventing unauthorized access to the attendance records.

Keywords: Face Recognition, Attendance System, AI Algorithms, Automation, Scalable Solution