Face recognition Using the Haar Cascade Classifier and Local Binary Patterns Histogram Algorithms to Detect and Identify Faces for Attendance
Abstract
In the era of globalization, especially in the field
of education, student attendance tracking holds significant value
for monitoring and managing participation within the teaching
and learning process. Face detection and identification play a
pivotal role in various modern technological applications, such
as facial recognition and facial expression analysis. In the
development of this system, a biometric approach using face
recognition is employed, leveraging the Haar Cascade Classifier
method for face detection in images, alongside the Local Binary
Pattern Histogram (LBPH) method for facial identification
through texture patterns. The system's implementation is
conducted using the Python programming language and the
OpenCV library. Testing is performed to recognize faces under
diverse conditions, including variations in distance, light
intensity, facial orientation, background, and accessories. Face
detection and identification time range from 0.04 - 0.08 seconds,
and a distance range of 30 cm - 150 cm.
Keyword — Face Recognition, Haar Cascade Classifier,Local Binary Pattern Histogram (LBPH), OpenCV
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