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Kinship Verification Through Facial Images Using CNN-Based Features

Abstract : The use of facial images in the kinship verification is a challenging research problem in soft biometrics and computer vision. In our work, we present a kinship verification system that starts with pair of facial images of the child and parent, then as a final result is determine whether two persons have a kin relation or not. our approach contains five steps as follows: (i) the face preprocessing step to get aligned and cropped facial images of the pair (ii), extracting deep features based on the deep learning model called Visual Geometry Group (VGG) Face, (iii) applying our proposed pair feature representation function alongside with a features normalization, (iv) the use of Fisher Score (FS) to select the best discriminative features, (v) decide whether there is a kinship or not based on the Support Vector Machine (SVM) classifier. We conducted several experiments to demonstrate the effectiveness of our approach that we tested on five benchmark databases (Cornell KinFace, UB KinFace, Familly101, KinFace W-I, and KinFace W-II). Our results indicate that our system is robust compared to other existing approaches.
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Submitted on : Tuesday, March 24, 2020 - 5:48:56 PM
Last modification on : Monday, April 6, 2020 - 5:16:01 AM

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Abdelhakim Chergui, Salim Ouchtati, Sébastien Mavromatis, Salah Bekhouche, Mohamed Lashab, et al.. Kinship Verification Through Facial Images Using CNN-Based Features. Traitement du Signal, Lavoisier, 2020, 37 (1), pp.1-8. ⟨10.18280/ts.370101⟩. ⟨hal-02517794⟩

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