Age estimation from face images: Human vs. machine performance
Age estimation from face images: Human vs. machine performance
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DOI：
10.1109/icb.2013.6613022
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Hu Han;C. Otto;Anil K. Jain
中科院分区：
文献类型：
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作者：
Hu Han;C. Otto;Anil K. Jain
由于在执法、安全控制和人机交互方面的各种潜在应用，人们对面部图像的自动年龄估计越来越感兴趣。然而，尽管在自动年龄估计方面取得了进展，但它仍然是一个具有挑战性的问题。这是因为面部衰老过程不仅由内在因素（如遗传因素）决定，也由外在因素（如生活方式、表情和环境）决定。因此，由于面部衰老的速度不同，相同年龄的不同人会有完全不同的外观。我们提出了一种自动年龄估计的分层方法，并提供了衰老如何影响个体面部成分的分析。在FG-NET、MORPH Album2和PCSO数据库上的实验结果表明，在自动年龄估计中，眼睛和鼻子比其他面部成分的信息量更大。我们还研究了人类使用众包收集的数据估计年龄的能力，并表明我们的方法在5年平均绝对误差（MAE）内的累积得分（CS）优于人类提供的年龄估计。
There has been a growing interest in automatic age estimation from facial images due to a variety of potential applications in law enforcement, security control, and human-computer interaction. However, despite advances in automatic age estimation, it remains a challenging problem. This is because the face aging process is determined not only by intrinsic factors, e.g. genetic factors, but also by extrinsic factors, e.g. lifestyle, expression, and environment. As a result, different people with the same age can have quite different appearances due to different rates of facial aging. We propose a hierarchical approach for automatic age estimation, and provide an analysis of how aging influences individual facial components. Experimental results on the FG-NET, MORPH Album2, and PCSO databases show that eyes and nose are more informative than the other facial components in automatic age estimation. We also study the ability of humans to estimate age using data collected via crowdsourcing, and show that the cumulative score (CS) within 5-year mean absolute error (MAE) of our method is better than the age estimates provided by humans.