Facial age estimation through self-paced learning

Abstract

This paper proposes an age estimation algorithm in Self-Paced Learning (SPL) framework. Facial samples in the training set inherently include both easy and complex images, which is caused by both the characteristic of age and the variation of pose or expression. Furthermore, by randomly hiding patches in face region, data with different difficulty levels can be gradually used by SPL, in which Convolution Neural Network (CNN) is trained to give the estimation. Alternative Optimization Strategy (AVO), for the weight of CNN and the latent weight in SPL regularizer, is adopted in SPL framework. Experiments show that the proposed algorithm is able to give an accurate results especially under pose and expression variation.