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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