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Kinship Verification based Deep and Tensor Features through Extreme Learning Machine

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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.

Dates and versions

hal-03572289 , version 1 (14-02-2022)

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Oualid Laiadi, Abdelmalik Ouamane, Abdelhamid Benakcha, Abdelmalik Taleb-Ahmed, Abdenour Hadid. Kinship Verification based Deep and Tensor Features through Extreme Learning Machine. 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), May 2019, Lille, France. pp.735-738, ⟨10.1109/FG.2019.8756627⟩. ⟨hal-03572289⟩
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