Facial Landmarks and Face Detection in Python With OpenCv - Supiyandi Supiyandi; Icha Miranti Irzan; Risma Hidayati; Rosa Prahasti; Natria Selina (STIKes Ibnu Sina Ajibarang, 2024)
Face detection and facial landmarks are an important technique in the field of computer vision with a wide range of potential applications, including expression recognition, security systems, and human-computer interaction. This study explores the implementation of facial landmarks detection using Python and OpenCV, focusing on the use of the Haar Cascade algorithm for face detection and the Local Binary Features (LBF) model for the identification of landmarks. The proposed method implements real-time detection via webcam, capable of recognizing 68 important points on the human face. The results show that the approach using OpenCV and LBF models has good accuracy in detecting and tracking facial features in different lighting conditions and viewing angles. This research contributes to the development of efficient and reliable facial detection methods, with wide application potential in the fields of computer vision, security, and behavioral analysis.