Age estimation based on a single network with soft softmax of aging modeling

Abstract

In this paper, we propose a novel approach based on a single convolutional neural network (CNN) for age estimation. In our proposed network architecture, we first model the randomness of aging with the Gaussian distribution which is used to calculate the Gaussian integral of an age interval. Then, we present a soft softmax regression function used in the network. The new function applies the aging modeling to compute the function loss. Compared with the traditional softmax function, the new function considers not only the chronological age but also the interval nearby true age. Moreover, owing to the complex of Gaussian integral in soft softmax function, a look up table is built to accelerate this process. All the integrals of age values are calculated offline in advance. We evaluate our method on two public datasets: MORPH II and Cross-Age Celebrity Dataset (CACD), and experimental results have shown that the proposed method has gained superior performances compared to the state of the art.