Smart Attendance Monitoring System Based on Kernel Principal Component Analysis and Singular Value Decomposition

Abstract: Advances in programming face recognition have made numerous impacts in the evolving scene. A PC framework in my face recognition project will want to locate and recognize human faces in images or recordings captured by an observation camera quickly and precisely. Various calculations and procedures have been developed for working on the presentation of face recognition, but the idea to be implemented here is Deep Learning movements. Today, recording someone's presence is the most important thing for any organization. Someone's attendance at an office or association indicates that they are fulfilling their obligation to attend. This paper explains how to track participation in a simple and effective way. Face recognition provides a precise framework for dealing with ambiguous situations like fake participation. This framework uses an Open CV face recognition library for facial distinguishing proof and participation storage (Python). The picture is captured by the camera and sent to an information base organizer, which contains pictures that distinguish faces and calculate participation.

Keywords: OpenCV, Numpy, DLIB, Cmake, Face Detections, Face Recognition