Inheritable Fisher vector feature for kinship verification

Qingfeng Liu, Ajit Puthenputhussery, Chengjun Liu

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

25 Scopus citations

Abstract

An innovative inheritable Fisher vector feature (IFVF) method is presented in this paper for kinship verification. Specifically, Fisher vector is first derived for each image by aggregating the densely sampled SIFT features in the opponent color space. Second, a new inheritable transformation, which maximizes the similarity between kinship images while minimizes that between non-kinship images for each image pair simultaneously, is learned based on the Fisher vectors. As a result, the IFVF is derived by applying the inheritable transformation on the Fisher vector for each image. Finally, a novel fractional power cosine similarity measure, which shows its theoretical roots in the Bayes decision rule for minimum error, is proposed for kinship verification. Experimental results on two representative kinship data sets, namely the KinFaceW-I and the KinFaceW-II data sets, show the feasibility of the proposed method.

Original languageEnglish (US)
Title of host publication2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems, BTAS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479987764
DOIs
StatePublished - Dec 16 2015
Event7th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2015 - Arlington, United States
Duration: Sep 8 2015Sep 11 2015

Other

Other7th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2015
CountryUnited States
CityArlington
Period9/8/159/11/15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computer Science Applications
  • Biomedical Engineering

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