Facial Features Extraction by Grayscale Gradient and Circular Hough Transform

Abstract

Extraction of facial feature points is essential task for many kinds of applications of face images. The feasibility of facial features extraction is determined by not only its accuracy but processing time. Some applications require real-time detection of facial features. This research aims to propose a method of facial features extraction by an accelerated implementation of circular Hough transform with gradients and appearance evaluation by histogram of gradient features. Experiment using FERET database shows that the proposed method successfully extracted eyes, nose and mouth for 98.44%, 99.50% and 98.79% of frontal face images in the dataset.