Face recognition Using the Haar Cascade Classifier and Local Binary Patterns Histogram Algorithms to Detect and Identify Faces for Attendance - Purnaningsih , Ni Kadek Ayu; Kallista , Meta; Hasibuan , Faisal Candrasyah (eProceedings of Engineering, 2024)
In the era of globalization, especially in the fieldof education, student attendance tracking holds significant valuefor monitoring and managing participation within the teachingand learning process. Face detection and identification play apivotal role in various modern technological applications, suchas facial recognition and facial expression analysis. In thedevelopment of this system, a biometric approach using facerecognition is employed, leveraging the Haar Cascade Classifiermethod for face detection in images, alongside the Local BinaryPattern Histogram (LBPH) method for facial identificationthrough texture patterns. The system's implementation isconducted using the Python programming language and theOpenCV library. Testing is performed to recognize faces underdiverse conditions, including variations in distance, lightintensity, facial orientation, background, and accessories. Facedetection 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