Gait-based age estimation has been extensively studied for various applications because of its high practicality. In this paper, we propose a gait-based age estimation method using convolutional neural networks (CNNs). Because gait features vary depending on a subject’s attributes, i.e., gender and generation, we propose the following three CNN stages: (1) a CNN for gender estimation, (2) a CNN for age-group estimation, and (3) a CNN for age regression. We conducted experiments using a large population gait database and confirm that the proposed method outperforms state-of-the-art benchmarks.

中文翻译：

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使用多阶段卷积神经网络的基于步态的年龄估计

基于步态的年龄估计因其高度实用性而被广泛研究。在本文中，我们提出了一种使用卷积神经网络（CNN）的基于步态的年龄估计方法。由于步态特征随主体属性（即性别和世代）的不同而不同，因此我们提出以下三个CNN阶段：（1）用于性别估计的CNN，（2）用于年龄组估计的CNN和（3）CNN用于年龄回归。我们使用大量的步态数据库进行了实验，并证实了所提出的方法优于最新的基准测试。