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Feature Relationships Learning Incorporated Age Estimation Assisted by Cumulative Attribute Encoding

Qing Tian1,2,3,*, Meng Cao1,2, Tinghuai Ma1,2

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
School of Electrical and Electronic Engineering, University of Manchester, Manchester, M13 9PL, UK.

* Corresponding Author: Qing Tian. Email: email; email.

Computers, Materials & Continua 2018, 56(3), 467-482. 10.3970/cmc.2018.02197


The research of human facial age estimation (AE) has attracted increasing attention for its wide applications. Up to date, a number of models have been constructed or employed to perform AE. Although the goal of AE can be achieved by either classification or regression, the latter based methods generally yield more promising results because the continuity and gradualness of human aging can naturally be preserved in age regression. However, the neighbor-similarity and ordinality of age labels are not taken into account yet. To overcome this issue, the cumulative attribute (CA) coding was introduced. Although such age label relationships can be parameterized via CA coding, the potential relationships behind age features are not incorporated to estimate age. To this end, in this paper we propose to perform AE by encoding the potential age feature relationships with CA coding via an implicit modeling strategy. Besides that, we further extend our model to gender-aware AE by taking into account gender variance in aging process. Finally, we experimentally validate the superiority of the proposed methodology


Cite This Article

Q. . Tian, M. . Cao and T. . Ma, "Feature relationships learning incorporated age estimation assisted by cumulative attribute encoding," Computers, Materials & Continua, vol. 56, no.3, pp. 467–482, 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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