Relationships Self-Learning Based Gender-Aware Age Estimation


In biometrics research, face-appearance based age estimation (AE) becomes an important topic and has attracted a great deal of attention due to its potential applications. To achieve the goal of AE, a variety of methods have been proposed in the literature, among which the cumulative attribute (CA) coding based methods have achieved promising performance by preserving both ordinality and neighbor-similarity of ages. However, the sub-regressors responsible for regressing each of the CA coding elements are learned separately, while all of them are trained on the same dataset, leading to that potential correlation relationships of inter/intra-CA coding are not exploited. To this end, we herein propose a novel correlation learning method to model and utilize such inter/intra-CA relationships for AE, through self-learning from the training data. In addition, we extend the proposed method to perform gender-aware AE by further exploiting the correlations between and within gender groups. Furthermore, we introduce an alternating optimization algorithm for the proposed methods. Extensive experiments are conducted to demonstrate that the proposed methods can significantly improve the accuracy of AE, and more importantly that they can model well both inter/intra CA coding and gender relationships, regardless whether they are related (positive or negative) or not.

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  1. 1.

    In such case, we adopt the CA formulation in Eq. (2) to encode the \(y_{i}\) in Eq. (3).

  2. 2.

    Since in this work, we concentrate on exploring the correlations among the K-dimensional CA codings, so we define \(\Omega \in \mathbb {R}^{K\times K}\) as the column covariance matrix of the prior matrix-variate normal distribution on \(W \in \mathbb {R}^{d\times K}\), while introducing a d-order identity matrix \(I \in \mathbb {R}^{d\times d}\) as the row covariance matrix.

  3. 3.

    As reported in previous research [42, 44], the AAM, BIF and HoG visual features exhibit promising capacity in representing facial appearances as well as to evaluate the generalization ability of our method to feature representation variances, herein we extract various types of features from different face datasets for experiment.

  4. 4.

    \(MAE = \frac{1}{N}\sum _{i=1}^{N}|\widehat{l}_i-l_i|\) with \(l_i\) and \(\widehat{l}_i\) respectively being the actual and predicted age labels.


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This work was partially supported by the National Natural Science Foundation of China under Grants 61702273 and 61472186, the Natural Science Foundation of Jiangsu Province under Grant BK20170956, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 17KJB520022, the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Startup Foundation for Talents of Nanjing University of Information Science and Technology.

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Correspondence to Songcan Chen.

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Tian, Q., Cao, M., Chen, S. et al. Relationships Self-Learning Based Gender-Aware Age Estimation. Neural Process Lett 50, 2141–2160 (2019).

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  • Age estimation
  • Cumulative attribute
  • Correlation learning strategy
  • Gender-aware age estimation