Face is an individual's unique representation,
and therefore, we propose an automated system for
student attendance using face recognition. Face
recognition systems have significant applications,
particularly in security control systems. For instance, the
airport protection system relies on face recognition to
identify potential suspects, while the Federal Bureau of
Investigation (FBI) utilizes this technology for criminal
investigations. Our proposed approach begins with video
framing, initiated through a user-friendly interface. By
employing the Viola-Jones algorithm, we detect and
segment the region of interest (ROI) containing the face
from the video frame. In the preprocessing stage, we
perform image scaling as necessary to preserve
information integrity. Next, we apply median filtering to
eliminate noise and convert color images to grayscale. To
enhance image contrast, we implement contrast-limited
adaptive histogram equalization (CLAHE). In the face
recognition stage, we utilize enhanced local binary
pattern (LBP) and principal component analysis (PCA)
to extract facial image features. Subsequently, we record
the attendance of the recognized student, saving the data
in an Excel file. Unregistered students have the
opportunity to register on the spot, and notifications are
triggered if a student signs in more than once. The
recognition accuracy is 100% for high-quality images,
94.12% for low-quality images, and 95.76% for the Yale
face database when training with two images per person.
Keywords :
Face recognition system, Median filtering, CLACHE, LBP, andPCA Image Extraction.