University of Oulu

Oualid Laiadi, Abdelmalik Ouamane, Abdelhamid Benakcha, Abdelmalik Taleb-Ahmed, Abdenour Hadid, Tensor cross-view quadratic discriminant analysis for kinship verification in the wild, Neurocomputing, Volume 377, 2020, Pages 286-300, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2019.10.055

Tensor cross-view quadratic discriminant analysis for kinship verification in the wild

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Author: Laiadi, Oualid1; Ouamane, Abdelmalik2; Benakcha, Abdelhamid3;
Organizations: 1Laboratory of LESIA, University of Biskra, Algeria
2University of Biskra, Algeria
3Laboratory of LGEB, University of Biskra, Algeria
4IEMN DOAE UMR CNRS 8520 Laboratory, Polytechnic University of Hauts-de-France, France
5Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2020042822717
Language: English
Published: Elsevier, 2020
Publish Date: 2021-10-19
Description:

Abstract

This paper presents a new Tensor Cross-view Quadratic Discriminant Analysis (TXQDA) method based on the XQDA method for kinship verification in the wild. Many researchers used metric learning methods and have achieved reasonably good performance in kinship verification, none of these methods looks at the kinship verification as a cross-view matching problem. To tackle this issue, we propose a tensor cross-view method to train multilinear data using local histograms of local features descriptors. Therefore, we learn a hierarchical tensor transformation to project each pair face images into the same implicit feature space, in which the distance of each positive pair is minimized and that of each negative pair is maximized. Moreover, TXQDA was proposed to separate the multifactor structure of face images (i.e. kinship, age, gender, expression, illumination and pose) from different dimensions of the tensor. Thus, our TXQDA achieves better classification results through discovering a lowdimensional tensor subspace that enlarges the margin of different kin relation classes. Experimental evaluation on five challenging databases namely Cornell KinFace, UB KinFace, TSKinFace, KinFaceW-II and FIW databases, show that the proposed TXQDA significantly outperforms the current state of the art.

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Series: Neurocomputing
ISSN: 0925-2312
ISSN-E: 1872-8286
ISSN-L: 0925-2312
Volume: 377
Pages: 286 - 300
DOI: 10.1016/j.neucom.2019.10.055
OADOI: https://oadoi.org/10.1016/j.neucom.2019.10.055
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: © 2019 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/