Face Detection Technique can detect faces in images and achieve high performance on detec- tion rates. Face Detection technologies and applications have recently developed and improved as a result of advancements in computer software and hardware. Nowadays, it is employed in every part of our lives. Face Detection technologies are being employed in a wide range of fields, including access control, law enforcement, entertainment, personal safety, and many more. Based on OpenCV and a machine learning software library that is mainly oriented at com- puter vision. This library consists of more than 2500 algorithms which contain machine learning tools for classification and clustering, image processing, and vision algorithms. OpenCV library has several built-in pre-trained classifiers developed individually for detecting faces, eyes, and smiles. In this project, we choose the Haar cascade Classifier rather than the Local Binary Pattern (LBP) as one of the OpenCV algorithms. LBP features concentrate on detection hit rate and detection speed. As a result, we selected the Haar cascade Classifier because we aimed for accuracy in detecting faces in images rather than the LBP speed rates. This algorithm is the most efficient and reliable for the implementation phase of face detection systems. From our tested, we can see that the HAAR cascade classifier finds all the faces within an average of 0.07 sec, and the accuracy rate is also %100. It has been a success that we have accomplished most of what we had planned at the beginning of the project. Finally, we verified that the system worked as planned by testing it.