Computational prediction of facial aesthetics has attracted ever-increasing research focus. The key challenge lies in extracting discriminative and perception-aware features to characterize the facial beautifulness. To this end, the existing schemes simply adopt a direct feature mapping, which relies on handcraft-designed low-level features that cannot reflect human-level aesthetic perception. In this paper, we present a systematic framework towards designing biology-inspired, discriminative representation for facial aesthetic prediction. First, we design a group of biological experiments that adopt eye tracker to identify spatial regions of interest during the facial aesthetic judgments of subjects, which forms a Bio-inspired Facial Aesthetic Ontology (Bio-FAO) and is made public available. Second, we adopt the cutting-edge convolutional neural network to train a set of Bio-inspired Attribute features, termed Bio-AttriBank, which forms a mid-level interpretable representation corresponding to the aforementioned Bio-FAO. For a given image, the facial aesthetic prediction is then formulated as a classification problem over the Bio-AttriBank descriptor responses, which well bridges the affective gap, and provides explainable evidences on why/how a face is beautiful or not. We have carried out extensive experiments on both JAFFE and FaceWarehouse datasets. Superior performance gains in the experiments have demonstrated the merits of the proposed scheme.

中文翻译：

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面向面部美学预测的仿生深度属性学习

面部美学的计算预测吸引了越来越多的研究焦点。关键的挑战在于提取具有辨别力和感知意识的特征来表征面部美丽。为此，现有的方案简单地采用直接特征映射，这依赖于手工设计的低级特征，不能反映人类水平的审美感知。在本文中，我们提出了一个系统框架，旨在为面部美学预测设计受生物学启发的判别式表示。首先，我们设计了一组生物实验，采用眼动仪识别受试者面部审美判断中感兴趣的空间区域，形成仿生面部美学本体（Bio-FAO）并公开发布。第二，我们采用最先进的卷积神经网络来训练一组仿生属性特征，称为 Bio-AttriBank，形成与上述 Bio-FAO 相对应的中级可解释表示。对于给定的图像，面部美学预测然后被公式化为 Bio-AttriBank 描述符响应的分类问题，这很好地弥合了情感差距，并提供了关于为什么/如何一张脸美丽或不美丽的可解释证据。我们对 JAFFE 和 FaceWarehouse 数据集进行了大量实验。实验中的卓越性能提升证明了所提出方案的优点。然后，面部美学预测被表述为 Bio-AttriBank 描述符响应的分类问题，这很好地弥合了情感差距，并提供了关于为什么/如何解释一张脸美丽与否的可解释证据。我们对 JAFFE 和 FaceWarehouse 数据集进行了大量实验。实验中的卓越性能提升证明了所提出方案的优点。然后，面部美学预测被表述为 Bio-AttriBank 描述符响应的分类问题，这很好地弥合了情感差距，并提供了关于为什么/如何解释一张脸美丽与否的可解释证据。我们对 JAFFE 和 FaceWarehouse 数据集进行了大量实验。实验中的卓越性能提升证明了所提出方案的优点。