Deep probabilities for age estimation

Human age can provide important demographic information. In this paper, we tackle the estimation of age in face images with probabilities. The design of the proposed method is based on the relative order of age labels in the database. The age estimation problem is transformed into a series of binary classifications achieved by convolution neural network. Each classifier is used to judge whether the age of input image is larger than a certain age and the estimated age is obtained by adding probability values of these classification problems. The proposed method: Deep Probabilities (DP) of facial age shows improvements over direct regression and multi-classification methods.