Abstract
Age estimation has wide applications in video surveillance, social networking, and human-computer interaction. Many of the published approaches simply treat age
estimation as an exact age regression problem, and thus do
not leverage a distribution’s robustness in representing labels with ambiguity such as ages. In this paper, we propose
a new loss function, called mean-variance loss, for robust
age estimation via distribution learning. Specifically, the
mean-variance loss consists of a mean loss, which penalizes
difference between the mean of the estimated age distribution and the ground-truth age, and a variance loss, which
penalizes the variance of the estimated age distribution to
ensure a concentrated distribution. The proposed meanvariance loss and softmax loss are jointly embedded into
Convolutional Neural Networks (CNNs) for age estimation.
Experimental results on the FG-NET, MORPH Album II,
CLAP2016, and AADB databases show that the proposed
approach outperforms the state-of-the-art age estimation
methods by a large margin, and generalizes well to image
aesthetics assessment