Computer vision based automated attendance system using face recognition approach.

Human face recognition is a branch of biometric authentication that has been commonly used in a variety of applications, including video monitoring, human-computer interaction, door control, and network security. This project describes a method for a Student Attendance System that uses Python and Computer Vision to combine with face recognition technology. It recognizes the faces in the picture and compares them to the database's enrolled faces. The attendance register is identified as present when a registered face is found in the acquired picture collections. Since the device is based on an open-source image processing library, it is not hardware or software vendor dependent. Computer vision is a broad field of study that involves extracting information from images. Human activity identification is an example of an area that is currently undergoing extensive study. This model has a wide range of applications in both academic study and real-world applications. A two-model solution using a computer vision and face recognition module is proposed in this paper. The feature vectors for each video are gathered using computer vision, and the video operation is classified using the face recognition module. [ABSTRACT FROM AUTHOR]