University of Oulu

O. Laiadi, A. Ouamane, A. Benakcha, A. Taleb-Ahmed and A. Hadid, "Kinship Verification based Deep and Tensor Features through Extreme Learning Machine," 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France, 2019, pp. 1-4. doi: 10.1109/FG.2019.8756627

Kinship verification based deep and tensor features through extreme learning machine

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Author: Laiadi, Oualid1,2; Ouamane, Abdelmalik3; Benakcha, Abdelhamid4;
Organizations: 1Laboratory of LESIA, University of Biskra, Algeria
2IEMN DOAE UMR CNRS 8520 Laboratory, Polytechnic University of Hauts-de-France, France
3University of Biskra, Algeria
4Laboratory of LGEB, University of Biskra, Algeria
5Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202003248968
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-03-24
Description:

Abstract

Checking the kinship of facial images is a difficult research topic in computer vision that has attracted attention in recent years. The methods suggested so far are not strong enough to predict kinship relationships only by facial appearance. To mitigate this problem, we propose a new approach called Deep-Tensor+ELM to kinship verification based on deep (VGG-Face descriptor) and tensor (BSIF-Tensor & LPQ-Tensor using MSIDA method) features through Extreme Learning Machine (ELM). While ELM aims to deal with small size training features dimension, deep and tensor features are proven to provide significant enhancement over shallow features or vector-based counterparts. We evaluate our proposed method on the largest kinship benchmark namely FIW database using four Grandparent-Grandchild relations (GF-GD, GF-GS, GM-GD and GM-GS). The results obtained are positively compared with some modern methods, including those that rely on deep learning.

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ISBN: 978-1-72810-089-0
ISBN Print: 978-1-7281-0090-6
Pages: 1 - 4
Article number: 8756627
DOI: 10.1109/FG.2019.8756627
OADOI: https://oadoi.org/10.1109/FG.2019.8756627
Host publication: 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, 14-18 May 2019, Lille, France
Conference: IEEE International Conference on Automatic Face and Gesture Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
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