Age-Invariant Recognition for Classifying Human Faces by Fusion of Shape and Texture Features

Abstract

Face recognition plays an important role in identifying a person from a set of test imagesused for personal identification, but an individual poses a serious problem in recognizing the facial images whenever age varies. So to overcome this problem, face recognition system is developed which is invariable of age. In support to that a paper was published by covering age contemplation. In the proposed system the problem was attended by fusion of phase congruency and local binary patterns. The advantages of the proposed system are, it improves the accuracy of recognizing the facial expressions in different pose and appearances, it works accurately without contemplation of age, and it can also work in Broad training set. Finally by implementing this we can recognize an individual without depending on age with resistance to pose variations and proper illuminations and gives a better computational efficiency.