Face Detection Using Haar Cascade Algorithm
Shahad salh Ali, Jamila Harbi Al' Ameri, Thekra Abbas
Abstract
Nowadays, machines are becoming friendlier and can react to people in a faster way. Human face detection has attractive wide spread interest in recent years and become a vital factor in Human Machine Interaction and one of the most popular topics in computer vision and biometrics. The aim of this paper is to detect human face from an image containing simple and complex backgrounds using the Haar cascade algorithm. The three principal of this algorithm are: Haar-like Filter, Integral Image and the AdaBoost cascade classifier. The available Labeled Faces in the Wild (LFW) is used as the research dataset in support with OpenCV library and python programming. Our experiments show that our proposed method confirms the state-of-the-arts.