Facial Landmark Detection and Estimation under Various Expressions and Occlusions

Landmark localization is one of the fundamental approaches to facial expressions recognition, occlusions detection and face alignments. It plays a vital role in many applications in image processing and computer vision. The acquisition conditions such as expression, occlusion and background complexity affect the landmark localization performance, which subsequently lead to system failure. In this paper, the writers bestowed the challenges of various landmark detection techniques, number of landmark points and dataset types been employed from the existing literatures. However, advance technique for facial landmark detection under various expressions and occlusions was presented. This was carried out using Point Distribution Model (PDM) to estimate the occluded part of the facial regions and detect the face. The proposed method was evaluated using University Milano B icocca Database (UMB). This approach gave more promising result when compared to several previous works. In conclusion, the technique detected images despite varieties of occlusions and expressions. It can further be applied on images with different poses and illumination variations.