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Neighborhood min distance descriptor for kinship verification

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Abstract

The verification of parental and family relationships based on the facial appearance of subjects is a recent topic that attracted the attention of the computer vision research community. So many feature descriptors for facial images have been proposed, yet they are still unable to describe the similarity level of pairs of facial images in kinship datasets, because most of these images suffer from the illumination and expression variations. In this paper we propose a simple and effective descriptor for pairs of images, this descriptor is designed to be more robust to variations in expression, the idea of the descriptor is to compare each pixel in the first image of the pair with the neighboring pixels in the second image in terms of the euclidean distance on the RGB color space, the minimal distance in this neighborhood is then added to the feature vector of the pair. Experiments on the size of the neighborhood with various classifiers were conducted using the KinFaceW and Cornell-KinFace datasets, results demonstrated that this descriptor outperforms state-of-the-art approaches in five out of eight subsets of the KinFaceW and on the Cornell-KinFace dataset.

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Notes

  1. The white outer layer of the eyeball

  2. Facial expression, pose and transformation

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Authors and Affiliations

  1. Laboratory for Research in Artificial Intelligence, Computer Science Department, University of Science and Technology Houari Boumediene (USTHB), BP 32 El-Alia, Bab Ezzouar, Algiers, 16111, Algeria

    Abdellah Sellam & Hamid Azzoune

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  1. Abdellah Sellam
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  2. Hamid Azzoune
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Correspondence to Abdellah Sellam.

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Sellam, A., Azzoune, H. Neighborhood min distance descriptor for kinship verification. Multimed Tools Appl 79, 20861–20880 (2020). https://doi.org/10.1007/s11042-020-08906-6

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  • DOI: https://doi.org/10.1007/s11042-020-08906-6

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