Open Access

ARTICLE

Feature Relationships Learning Incorporated Age Estimation Assisted by Cumulative Attribute Encoding

Qing Tian^1,2,3,*, Meng Cao^1,2, Tinghuai Ma^1,2

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

* Corresponding Author: Qing Tian. Email: ; .

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

Abstract

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

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Keywords

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