Rights statement: This is the author’s version of a work that was accepted for publication in Physics Reports. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physics Reports, 373, 4-5, 2003 DOI: 10.1016/S0370-1573(02)00269-7
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Are mid-air dynamic gestures applicable to user identification?
AU - Liu, Heng
AU - Dai, Liangliang
AU - Hou, Shudong
AU - Han, Jungong
AU - Liu, Hongshen
N1 - This is the author’s version of a work that was accepted for publication in Physics Reports. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physics Reports, 373, 4-5, 2003 DOI: 10.1016/S0370-1573(02)00269-7
PY - 2018/4/18
Y1 - 2018/4/18
N2 - Abstract Unlike the existing gesture related research predominantly focusing on gesture recognition (classification), this work explores the feasibility and the potential of mid-air dynamic gesture based user identification through presenting an efficient bidirectional GRU (Gated Recurrent Unit) network. From the perspective of the feature analysis from the Bi-GRU network used for different recognition tasks, we make a detailed investigation on the correlation and the difference between the gesture type features and the gesture user identity characteristics. During this process, two unsupervised feature representation methods – PCA and hash ITQ (Iterative Quantization) are fully used to perform feature reduction and feature binary coding. Experiments and analysis based on our dynamic gesture data set (60 individuals) exemplify the effectiveness of the proposed mid-air dynamic gesture based user identification approach and clearly reveal the relationship between the gesture type features and the gesture user identity characteristics.
AB - Abstract Unlike the existing gesture related research predominantly focusing on gesture recognition (classification), this work explores the feasibility and the potential of mid-air dynamic gesture based user identification through presenting an efficient bidirectional GRU (Gated Recurrent Unit) network. From the perspective of the feature analysis from the Bi-GRU network used for different recognition tasks, we make a detailed investigation on the correlation and the difference between the gesture type features and the gesture user identity characteristics. During this process, two unsupervised feature representation methods – PCA and hash ITQ (Iterative Quantization) are fully used to perform feature reduction and feature binary coding. Experiments and analysis based on our dynamic gesture data set (60 individuals) exemplify the effectiveness of the proposed mid-air dynamic gesture based user identification approach and clearly reveal the relationship between the gesture type features and the gesture user identity characteristics.
KW - Gesture based user identification
KW - Gesture user identity characteristics
KW - Bi-GRU
KW - Mid-air dynamic gestures
U2 - 10.1016/j.patrec.2018.04.026
DO - 10.1016/j.patrec.2018.04.026
M3 - Journal article
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
SN - 0167-8655
ER -