Human Face Recognition Applying Haar Cascade Classifier * F.M. Javed Mehedi Shamrat 1 , Anup Majumder 2 , Probal Roy Antu 3 , Saykot Kumar Barmon 4 , Itisha Nowrin 5 , Rumesh Ranjan 6 1 Dept. Of Software Engineering, Daffodil International University 2 Dept. Of Computer Science and Engineering, Jahangirnagar University 3, 4, 5 Dept. Of Computer Science and Engineering, Daffodil International University 6 Department of Plant Breeding and Genetics, Punjab Agriculture University, Punjab, India 1* javedmehedicom@gmail.com 2 anupmajumder@juniv.edu 3 roy15-7171@diu.edu.bd 4 saykot15-6956@diu.edu.bd 5 nowrin.turna@gmail.com 6 rumeshranjan@pau.edu Abstract. Human face recognition is distinguished by a method of identifying facts or confirmation that tests personality. The technique essentially relies on two stages, one is face identification, and another is face recognition. Facial recognition applies to a PC device with a few implementations in which human faces can be identified in pictures. Usually, facial identification is achieved by using 'right' data from full-frontal facial photographs. While there are, as a general rule, sufficient situations where full frontal faces will not be available, a valid example is the blemished faces that come from CCTV cameras. Subsequently, the use of fractional facial data as tests is still, to a large extent, an unexplored field of research on the PC-based face recognition problem. In this research, through using incomplete facial evidence, we concentrate on face recognition. By implementing critical analysis to evaluate the presentation of AI using the Haar Cascade Classifier, we proposed and built our framework. Three phases of the proposed face detection method involve the Face Data Gathering (FDG) process, Train the Stored Image (TSI) phase, Face Recognition using the Local (FRUL) Binary Patterns Histograms (LBPH) algorithm, and this classifier computation was tested by splitting it into four phases. In this analysis, to complete the detection phase, we apply Haar feature selection, generating an integral image, Adaboost preparing, Cascading Classifiers. To complete this venture's human protection facial recognition framework with face detection, we used Local Binary Patterns Histograms (LBPH) estimate. In LBPH, a few parameters are used and a dataset is obtained by implementing an algorithm. By adding the LBPH operation and extracting the histograms, I got the Final computational part. "Image Processing Based Human Face Recognition Using Haar Cascade Classifier" Image Processing Based Human Face Recognition Using Haar Cascade Classifier. International Conference on Pervasive Computing and Social Networking [ICPCSN 2021] Salem, Tamil Nadu, India, 19-20, March 2021 Pre-Print