Facial Age Estimation under the Terms of Local Latency Using Weighted Local Binary Pattern and Multi-Layer Perceptron

Age estimation is one of the main problems in the framework of pattern recognition which aims to predict the age of an individual according to his (her) facial features. The difficulty of age estimation will be increased when several parts of facial image are covered by the local latency such as sun glasses or scarf. In this paper a new facial age estimation method is proposed to estimate the age of an individual under the terms of local latency. This paper proposes a new Local Binary Pattern (LBP)-based feature extraction method which is combined with a weighting scheme to assign high weights to general LBP feature elements (parts of facial image without local latency) whereas assigns low weights to the feature elements of facial image which are covered by the local latency. In the proposed method, the weighted feature elements are employed in Multi-Layer Perceptron (MLP) model for age estimation. Evaluation results of the proposed method on three aging datasets such as FG-NET, MORPH and UCI which contain facial image under the local latency proves the ability of the proposed method in age estimation problem even under the terms of local latency.