The level of taste liking is an important measure for a number of applications such as the prediction of long-term consumer acceptance for different food and beverage products. Based on the fact that facial expressions are spontaneous, instant and heterogeneous sources of information, this paper aims to automatically estimate the level of taste liking through facial expression videos. Instead of using handcrafted features, the proposed approach deep learns the regional expression dynamics, and encodes them to a Fisher vector for video representation. Regional Fisher vectors are then concatenated, and classified by linear SVM classifiers. The aim is to reveal the hidden patterns of taste-elicited responses by exploiting expression dynamics such as the speed and acceleration of facial movements. To this end, we have collected the first large-scale beverage tasting database in the literature. The database has 2,970 videos of taste-induced facial expressions collected from 495 subjects. Our large-scale experiments on this database show that the proposed approach achieves an accuracy of 70.37 percent for distinguishing between three levels of taste-liking. Furthermore, we assess the human performance recruiting 45 participants, and show that humans are significantly less reliable for estimating taste appreciation from facial expressions in comparison to the proposed method.

中文翻译：

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通过面部表情动力学自动估计口味喜好

口味喜好水平是许多应用的重要衡量标准，例如预测消费者对不同食品和饮料产品的长期接受度。基于面部表情是自发的、即时的和异构的信息源这一事实，本文旨在通过面部表情视频自动估计口味喜好程度。所提出的方法不是使用手工制作的特征，而是深度学习区域表达动态，并将它们编码为 Fisher 向量以进行视频表示。然后连接区域 Fisher 向量，并通过线性 SVM 分类器进行分类。目的是通过利用面部运动的速度和加速度等表情动态来揭示味觉诱发反应的隐藏模式。为此，我们收集了文献中第一个大规模的饮料品尝数据库。该数据库包含从 495 名受试者中收集的 2,970 个味觉诱发面部表情视频。我们对该数据库的大规模实验表明，所提出的方法在区分三个口味喜好方面的准确率达到了 70.37%。此外，我们评估了招募 45 名参与者的人类表现，并表明与所提出的方法相比，人类从面部表情估计味觉欣赏的可靠性​​明显较低。37% 用于区分三种口味喜好。此外，我们评估了招募 45 名参与者的人类表现，并表明与所提出的方法相比，人类从面部表情估计味觉欣赏的可靠性​​明显较低。37% 用于区分三种口味喜好。此外，我们评估了招募 45 名参与者的人类表现，并表明与所提出的方法相比，人类在估计面部表情的味觉欣赏方面的可靠性要低得多。