Facial expression data constructed with Kinect and their clustering stability

Angdy Erna, Linli Yu, Kaikai Zhao, Wei Chen, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationActive Media Technology - 10th International Conference, AMT 2014, Proceedings
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319099118
Publication statusPublished - 2014
Event10th International Conference on Active Media Technology, AMT 2014 - Warsaw, Poland
Duration: Aug 11 2014Aug 14 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8610 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Conference on Active Media Technology, AMT 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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