University of Oulu

A. Chergui, S. Ouchtati, S. Mavromatis, S. E. Bekhouche and J. Sequeira, "Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images," 2019 5th International Conference on Frontiers of Signal Processing (ICFSP), Marseille, France, 2019, pp. 82-87, doi: 10.1109/ICFSP48124.2019.8938055

Investigating deep CNNs models applied in kinship verification through facial images

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Author: Chergui, Abdelhakim1; Ouchtati, Salim1; Mavromatis, Sebastien2;
Organizations: 1Electronics Research Laboratory of Skikda (LRES), Skikda University. Algeria
2LIS Laboratory (UMR CNRS 7020), Aix Marseille University, France
3Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-06-25


The kinship verification through facial images is ana ctive research topic due to its potential applications. In this paper, we propose an approach which takes two images as input then give kinship result (kinship / No-kinship) as an output. our approach based on the deep learning model (ResNet) for the feature extraction step, alongside with our proposed pair feature representation function and RankFeatures (Ttest) for feature selection to reduce the number of features finally we use the SVM classifier for the decision of kinship verification. The approach contains three steps which are: (1) face preprocessing, (2) deep features extraction and pair features representation (3) Classification. Experiments are conducted on five public databases. The experimental results show that our approach is comparable with existed approaches.

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ISBN: 978-1-7281-5258-5
ISBN Print: 978-1-7281-5259-2
Pages: 82 - 87
Article number: 8938055
DOI: 10.1109/ICFSP48124.2019.8938055
Host publication: 2019 5th International Conference on Frontiers of Signal Processing (ICFSP)
Conference: International Conference on Frontiers of Signal Processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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