Convolutional Neural Networks for Subjective Face Attributes
Convolutional Neural Networks for Subjective Face Attributes
复制标题
用于主观人脸属性的卷积神经网络
DOI：
10.1016/j.imavis.2018.06.010
复制
发表时间：
2018
影响因子：
4.7
通讯作者：
Scheirer, Walter J.
中科院分区：
文献类型：
--
作者：
McCurrie, Mel;Beletti, Fernando;Parzianello, Lucas;Westendorp, Allen;Anthony, Samuel;Scheirer, Walter J.
可描述的视觉面部属性现在在人类生物识别和情感计算中很常见，现有的算法甚至达到了足够的成熟度，可以用于商业产品。这些算法对面部外观的客观方面进行建模，例如头发和眼睛的颜色、表情以及面部几何形状的方面。一个自然的扩展，这还没有被研究到任何很大的程度，迄今为止，是建模的主观属性，被分配给一个纯粹基于视觉判断的脸的能力。例如，仅仅一眼，我们对一张脸的第一印象可能会让我们相信这个人很聪明，值得我们信任，甚至值得我们钦佩-不管这些属性背后的潜在真相。心理学家认为，这些判断是基于各种因素，如情绪状态，个性特征和其他相貌线索。但是，在这个方向上的工作导致了一个有趣的问题：我们如何为只有可测量行为的问题创建模型？在本文中，我们介绍了一个基于卷积神经网络的回归框架，该框架允许我们训练人群行为的预测模型以进行社会属性分配。在来自AFLW人脸数据库的图像上，这些模型表现出与人类人群评级的强相关性。
Describable visual facial attributes are now commonplace in human biometrics and affective computing, with existing algorithms even reaching a sufficient point of maturity for placement into commercial products. These algorithms model objective facets of facial appearance, such as hair and eye color, expression, and aspects of the geometry of the face. A natural extension, which has not been studied to any great extent thus far, is the ability to model subjective attributes that are assigned to a face based purely on visual judgments. For instance, with just a glance, our first impression of a face may lead us to believe that a person is smart, worthy of our trust, and perhaps even our admiration — regardless of the underlying truth behind such attributes. Psychologists believe that these judgments are based on a variety of factors such as emotional states, personality traits, and other physiognomic cues. But work in this direction leads to an interesting question: how do we create models for problems where there is only measurable behavior? In this paper, we introduce a convolutional neural network-based regression framework that allows us to train predictive models of crowd behavior for social attribute assignment. Over images from the AFLW face database, these models demonstrate strong correlations with human crowd ratings.
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