ANALYSIS OF APPLICATION HAAR CASCADE CLASSIFIER AND LOCAL BINARY PATTERN HISTOGRAM ALGORITHM IN RECOGNIZING FACES WITH REAL-TIME GRAYSCALE IMAGES USING OPENCV - Rio Aditya Pahlevi; Bayu Setiaji (Informatika, Universitas Jenderal Soedirman, 2023)
Face detection and recognition systems have been developed with the various application of algorithms. Based on the literature study that has been carried out, the researcher will analyze the performance between the HCC (Haar Cascade Classifier) ​​and LBPH (Local Binary Pattern Histogram) Algorithms with real-time grayscale images using the OpenCV library. The test is carried out based on a sample of facial images with external conditions in the form of lighting conditions which are divided into morning, afternoon, and evening, as well as varying face rotation angles. Parameters observed were accuracy values, FAR (False Acceptance Rate), and FRR (False Rejection Rate). Based on the results of the tests that have been carried out, the average value of accuracy is 56%, while the average value of FAR is 22% and FRR is 23%. Judging from the average accuracy value obtained is 56%, then to be able to be detected and recognized properly the face position must be in frontal condition and with normal lighting. Thus, the final results of this study can be considered for other researchers who want to use a similar algorithm to develop a detection and recognition system.