Deep label refinement for age estimation

doi:10.1016/j.patcog.2019.107178

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Deep label refinement for age estimation

2020-04-01

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Zhenan Sun (Zhenan Sun)

DOI：10.1016/j.patcog.2019.107178

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Abstract

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Age estimation of unknown persons is a challenging pattern analysis task due to the lack of training data and various ageing mechanisms for different individuals. Label distribution learning-based methods usually make distribution assumptions to simplify age estimation. However, since different genders, races and/or any other characteristics may influence facial ageing, age-label distributions are often complicated and difficult to model parametrically. In this paper, we propose a label refinery network (LRN) with two concurrent processes: label distribution refinement and slack regression refinement. The label refinery network aims to learn age-label distributions progressively in an iterative manner. In this way, we can adaptively obtain the specific age-label distributions for different facial images without making strong assumptions on the fixed distribution formulations. To further utilize the correlations among age labels, we propose a slack regression refinery to convert the age-label regression model into an age-interval regression model. Extensive experiments on three popular datasets, namely, MORPH Album2, ChaLearn15 and MegaAge-Asian, demonstrate the superiority of our method. (C) 2019 Published by Elsevier Ltd.

Keywords:

Age estimation

Deep learning

Convolutional neural networks

Label distribution learning

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