How Confident Are You in Your Estimate of a Human Age? Uncertainty-aware Gait-based Age Estimation by Label Distribution Learning
How Confident Are You in Your Estimate of a Human Age? Uncertainty-aware Gait-based Age Estimation by Label Distribution Learning
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DOI：
10.1109/ijcb48548.2020.9304914
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发表时间：
2020-09
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影响因子：
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通讯作者：
Atsuya Sakata;Yasushi Makihara;Noriko Takemura;D. Muramatsu;Y. Yagi
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作者：
Atsuya Sakata;Yasushi Makihara;Noriko Takemura;D. Muramatsu;Y. Yagi
基于步态的年龄估计是许多应用的关键技术之一（例如，寻找走失的儿童/老年流浪者）。众所周知，年龄估计不确定性高度依赖于年龄（即，通常对于儿童来说较小，而对于成人/老年人来说较大），并且了解上述应用的不确定性是重要的。因此，我们提出了一种方法的不确定性意识的步态为基础的年龄估计，通过引入标签分布学习框架。更具体地说，我们设计了一个网络，该网络以基于外观的步态特征作为输入，并在整数年龄域中输出离散标签分布。在世界上最大的步态数据库OULP-Age上的实验表明，该方法能够成功地表示年龄估计的不确定性，并且性能优于或可与现有方法相媲美。
Gait-based age estimation is one of key techniques for many applications (e.g., finding lost children/aged wanders). It is well known that the age estimation uncertainty is highly dependent on ages (i.e., it is generally small for children while is large for adults/the elderly), and it is important to know the uncertainty for the above-mentioned applications. We therefore propose a method of uncertainty-aware gait-based age estimation by introducing a label distribution learning framework. More specifically, we design a network which takes an appearance-based gait feature as an input and outputs discrete label distributions in the integer age domain. Experiments with the world-largest gait database OULP-Age show that the proposed method can successfully represent the uncertainty of age estimation and also outperforms or is comparable to the state-of-the-art methods.