A novel inheritable color space with application to kinship verification

Qingfeng Liu, Ajit Puthenputhussery, Chengjun Liu

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

9 Scopus citations

Abstract

Anthropology studies discover that some genetic related facial features, which are inherited by children from their parents, can be used for kinship verification. This paper investigates an important inheritable feature - color and presents a novel inheritable color space (InCS) and a generalized InCS (GInCS) framework with application to kinship verification. Specifically, a novel color similarity measure (CSM) is first defined. Second, based on this similarity measure, a new inheritable color space (InCS) is derived by balancing the criterion of minimizing the distance between kinship pairs and the criterion of maximizing the distance between non-kinship pairs. Unlike conventional color spaces, e.g. the RGB color space, the proposed InCS, which is learned automatically from the data, captures the inheritable information between parent and child. Third, theoretical and empirical analysis show that the proposed InCS exhibits the decorrelation property, which is positively related to the performance of kinship verification. Robustness to the illumination variation is also discussed. Fourth, a generalized InCS framework is presented to extend the InCS from the pixel level to the feature level for improving the performance and the robustness to illumination variation. The proposed InCS is evaluated on several popular datasets, namely the KinFaceW-I dataset, the KinFaceW-II dataset, the UB KinFace dataset, and the Cornell KinFace dataset. Experimental results show that the proposed InCS is able to (i) improve the conventional color spaces such as RGB, YUV, YIQ color spaces by a large margin, (ii) achieve robustness to the illumination variation, and (iii) outperforms other popular methods.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Other

OtherIEEE Winter Conference on Applications of Computer Vision, WACV 2016
CountryUnited States
CityLake Placid
Period3/7/163/10/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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