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Facial attributes (\eg, age and attractiveness) estimation performance hasbeen greatly improved by using convolutional neural networks. However, existingmethods have an inconsistency between the training objectives and theevaluation metric, so they may be suboptimal. In addition, these methods alwaysadopt image classification or face recognition models with a large amount ofparameters, which carry expensive computation cost and storage overhead. Inthis paper, we firstly analyze the essential relationship between twostate-of-the-art methods (Ranking-CNN and DLDL) and show that the Rankingmethod is in fact learning label distribution implicitly. This result thusfirstly unifies two existing popular state-of-the-art methods into the DLDLframework. Second, in order to alleviate the inconsistency and reduce resourceconsumption, we design a lightweight network architecture and propose a unifiedframework which can jointly learn facial attribute distribution and regressattribute value. The effectiveness of our approach has been demonstrated onboth facial age and attractiveness estimation tasks. Our method achieves newstate-of-the-art results using the single model with 36$\times$ fewerparameters and 3$\times$ faster inference speed on facial age/attractivenessestimation. Moreover, our method can achieve comparable results as thestate-of-the-art even though the number of parameters is further reduced to0.9M (3.8MB disk storage).

Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation