TY - CHAP
T1 - Inheritable color space (InCS) and generalized InCS framework with applications to kinship verification
AU - Liu, Qingfeng
AU - Liu, Chengjun
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - This chapter presents a novel inheritable color space (InCS) and a generalized InCS (GInCS) framework for kinship verification. Unlike conventional color spaces, the proposed InCS is automatically derived by balancing the criterion of minimizing the distance between kinship images and the criterion of maximizing the distance between non-kinship images based on a new color similarity measure (CSM). Two important properties of the InCS, namely, the decorrelation property and the robustness to illumination variations property, are further presented through both theoretical and practical analyses. To utilize other inheritable features, a generalized InCS framework is then presented to extend the InCS from the pixel level to the feature level for improving the verification performance as well as the robustness to illumination variations. Experimental results on four representative datasets, the KinFaceW-I dataset, the KinFaceW-II dataset, the UB KinFace dataset, and the Cornell KinFace dataset, show the effectiveness of the proposed method.
AB - This chapter presents a novel inheritable color space (InCS) and a generalized InCS (GInCS) framework for kinship verification. Unlike conventional color spaces, the proposed InCS is automatically derived by balancing the criterion of minimizing the distance between kinship images and the criterion of maximizing the distance between non-kinship images based on a new color similarity measure (CSM). Two important properties of the InCS, namely, the decorrelation property and the robustness to illumination variations property, are further presented through both theoretical and practical analyses. To utilize other inheritable features, a generalized InCS framework is then presented to extend the InCS from the pixel level to the feature level for improving the verification performance as well as the robustness to illumination variations. Experimental results on four representative datasets, the KinFaceW-I dataset, the KinFaceW-II dataset, the UB KinFace dataset, and the Cornell KinFace dataset, show the effectiveness of the proposed method.
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U2 - 10.1007/978-3-319-52081-0_4
DO - 10.1007/978-3-319-52081-0_4
M3 - Chapter
AN - SCOPUS:85018464324
T3 - Intelligent Systems Reference Library
SP - 65
EP - 89
BT - Intelligent Systems Reference Library
PB - Springer Science and Business Media Deutschland GmbH
ER -