Facial Expression Data Constructed with Kinect and Their Clustering Stability
Facial Expression Data Constructed with Kinect and Their Clustering Stability
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DOI：
10.1007/978-3-319-09912-5_35
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发表时间：
2014-08
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影响因子：
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通讯作者：
A. Erna;Linli Yu;Kaikai Zhao;Wei Chen-;Einoshin Suzuki
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作者：
A. Erna;Linli Yu;Kaikai Zhao;Wei Chen-;Einoshin Suzuki
在本文中，我们使用Kinect面部跟踪应用程序构建了100人的面部表情基准数据，并研究了基准数据在聚类方面的稳定性。 Kinect 及其软件开发套件应用程序能够以低成本构建各种人体基准数据。我们设计了 25 种表情的多语言说明表，收集了 115 人的数据，并仔细检查和标记结果以构建数据。基准数据由 263,106 个实例组成，每个实例包括 6 个动画单元、11 个形状单元和一个图像文件，全部由应用程序提供。在 263,106 个实例中，我们将其中的 62,500 个标记为 25 个表达式之一，并研究了它们对 17 个特征的聚类稳定性。我们展示了最常用的聚类算法：k-means 实现了约 0.92 的平均正态互信息，作为我们面部表情数据稳定性的证据。
In this paper, we construct facial expression benchmark data of 100 persons using Kinect face tracking application and study the stability of the benchmark data in terms of clustering. Kinect with its Software Development Kit applications has enabled low-cost constructions of various benchmark data on humans. We devised multi-lingual instruction sheets on 25 expressions, collected data from 115 persons, and carefully inspected and labeled the outcome to construct the data. The benchmark data consist of 263,106 instances, each of which includes 6 animation units, 11 shape units, and an image file all provided by the application. Out of the 263,106 instances, we labeled 62,500 of them as 1 of the 25 expressions and investigated their clustering stabilities to the 17 features. We show that the most frequently used clustering algorithm: k-means achieves the average normal mutual information about 0.92 as an evidence of the stability of our facial expression data.