Will wrinkle estimate the face age?

The majority of current facial age estimation methods are based on appearance based features. However, wrinklebased research has not been widely addressed. In this paper, we propose a novel method based on multi-scale aging patterns (MAP). These directly extract the features from local patches without extensive geometric modelling. First, we locate facial landmarks by using the Face++ detector and then normalize the face by using a linear transformation. We define a face template which consists of ten predefined wrinkle regions. Then, for each region, we detect wrinkles and construct aging patterns by using the proposed methods. Finally, the age is estimated by implementing the support vector machine for regression. The performance of the algorithms is assessed by using mean absolute error (MAE) on the benchmark database - FERET. We observe that MAP produces the lowest MAE of 4.87 on FERET compared to the benchmark algorithms. Therefore, we conclude that wrinkle could be used as a feature on face age estimation. Future work would involve improvements of the algorithm by combining other descriptors such as non-wrinkle descriptor and appearance parameters.