Kinship verification based deep and tensor features through extreme learning machine
Laiadi, Oualid; Ouamane, Abdelmalik; Benakcha, Abdelhamid; Taleb-Ahmed, Abdelmalik; Hadid, Abdenour (2019-07-11)
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
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe202003248968
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [37575]