Mean-Variance Loss for Deep Age Estimation From a Face
Age estimation has broad application prospects of many fields, such as video surveillance, social networking, and human-computer interaction. However, many of the published age estimation approaches simply treat the age estimation as an exact age regression problem, and thus did 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 mean-variance loss and softmax loss are embedded jointly into Convolutional Neural Networks (CNNs) for age estimation, and the network weights are optimized via stochastic gradient descent (SGD) in an end-to-end learning way. Experimental results on a number of challenging face aging databases (FG-NET, MORPH Album II, and CLAP2016) show that the proposed approach outperforms the state-of-the-art methods by a large margin using a single model.
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Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Age Estimation | ChaLearn 2016 | Mean-Variance | e-error | 0.2867 | # 3 |