Facial Age Estimation based on Decision Level Fusion of AAM, LBP and Gabor Features

Abstract

In this paper a new hierarchical age estimation method based on decision level fusion of global and local features is proposed. The shape and appearance information of human faces which are extracted with active appearance models (AAM) are used as global facial features. The local facial features are the wrinkle features extracted with Gabor filters and skin features extracted with local binary patterns (LBP). Then feature classification is performed using a hierarchical classifier which is the combination of an age group classification and detailed age estimation. In the age group classification phase, three distinct support vector machines (SVM) classifiers are trained using each feature vector. Then decision level fusion is performed to combine the results of these classifiers. The detailed age of the classified image is then estimated in that age group, using the aging functions modeled with global and local features, separately. Aging functions are modeled with multiple linear regression. To make a final decision, the results of these aging functions are also fused in decision level. Experimental results on the FG-NET and PAL aging databases have shown that the age estimation accuracy of the proposed method is better than the previous methods.