Face Age Predictions by combining Local and Global Facial Features

Abstract

Aging is a gradual process, and it is difficult to see changes in the face as a whole, but signs of aging can be found in local areas. Therefore, fusing local face features on the basis of global features can improve the accuracy of age prediction. This paper proposes an age prediction method based on the combination of global facial features and local facial features. First, the local regions such as eyes, nose and mouth are cropped from the pre-processed facial image. Then, feature extraction is performed on the facial image and the cropped local area image, and the extracted features are subjected to feature selection, standardization and PCA dimension reduction. The processed features are then used for age prediction using multi-class support vector machines and neural networks. Experimental results show that this method outperforms previous methods.