Practical age estimation using deep label distribution learning

doi:10.1007/s11704-020-8272-4

Front. Comput. Sci.

2021, Vol. 15

Issue (3) : 153318 https://doi.org/10.1007/s11704-020-8272-4

RESEARCH ARTICLE

Practical age estimation using deep label distribution learning

Huiying ZHANG^1,², Yu ZHANG², Xin GENG²(

)

¹. Pujiang Institute, Nanjing Tech University, Nanjing 211200, China
². School of Computer Science and Engineering, Southeast University, Nanjing 211189, China

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Abstract

Age estimation plays an important role in humancomputer interaction system. The lack of large number of facial images with definite age label makes age estimation algorithms inefficient. Deep label distribution learning (DLDL) which employs convolutional neural networks (CNN) and label distribution learning to learn ambiguity from ground-truth age and adjacent ages, has been proven to outperform current state-of-the-art framework. However, DLDL assumes a rough label distribution which covers all ages for any given age label. In this paper, a more practical label distribution paradigm is proposed: we limit age label distribution that only covers a reasonable number of neighboring ages. In addition, we explore different label distributions to improve the performance of the proposed learning model. We employ CNN and the improved label distribution learning to estimate age. Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.

Keywords deep learning convolutional neural networks label distribution learning facial age estimation

Corresponding Author(s): Xin GENG

Just Accepted Date: 08 January 2020 Issue Date: 24 December 2020

Cite this article:

Huiying ZHANG,Yu ZHANG,Xin GENG. Practical age estimation using deep label distribution learning[J]. Front. Comput. Sci., 2021, 15(3): 153318.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-8272-4
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I3/153318

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