TY - GEN
T1 - Facial expression data constructed with Kinect and their clustering stability
AU - Erna, Angdy
AU - Yu, Linli
AU - Zhao, Kaikai
AU - Chen, Wei
AU - Suzuki, Einoshin
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-09912-5_35
DO - 10.1007/978-3-319-09912-5_35
M3 - Conference contribution
AN - SCOPUS:84905387530
SN - 9783319099118
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 421
EP - 431
BT - Active Media Technology - 10th International Conference, AMT 2014, Proceedings
PB - Springer Verlag
T2 - 10th International Conference on Active Media Technology, AMT 2014
Y2 - 11 August 2014 through 14 August 2014
ER -