Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images

Abdelhakim Chergui , Salim Ouchtati , Sébastien Mavromatis (1) , Salah Eddine Bekhouche , Jean Sequeira (1)

1 GMOD - MODélisation Géométrique

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Géométrie algorithmique [cs.CG]; Synthèse d'image et réalité virtuelle [cs.GR]; Traitement des images [eess.IV]

Abdelhakim Chergui, Salim Ouchtati, Sébastien Mavromatis, Salah Eddine Bekhouche, Jean Sequeira. Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images. 5th International Conference on Frontiers of Signal Processing (ICFSP 2019), Sep 2019, Marseille, France. ⟨hal-02400686⟩