Kinship verification is a challenging problem that recently attracted much interest in computer vision, this system has a number of applications such as organizing large collections of images and recognizing resemblances among humans and search for lost people. In this work, we propose a new method based on different descriptors mixed such as (LBP, LPQ, BSIF), and the Multi-Block (MB) representation. and we investigate the effect of different features representation for kinship verification, Moreover, the use of TTest to reduce the number of features and the support vector machine (SVM) for the kinship classification. Our approach consists of five stages: (1) features extraction, (2) face representation (3) features representation, (4) features selection and (5) classification. Our approach is tested on five datasets (Cornell, UB Kin Face, Familly 101, KinFac W-I and W-II). Our results are good comparable with other approaches.
Chergui Abdelhakim, Ouchtati Salim, Mavromatis Sebastien, Bekhouche Salah Eddine, Sequeira Jean, Zerrari Houssem
A4 Article in conference proceedings
Place of publication:
2019 International Conference on Networking and Advanced Systems (ICNAS)
A. Chergui, S. Ouchtati, S. Mavromatis, S. Eddine Bekhouche, J. Sequeira and H. Zerrari, “Kinship Verification using Mixed Descriptors and Multi Block Face Representation,” 2019 International Conference on Networking and Advanced Systems (ICNAS), Annaba, Algeria, 2019, pp. 1-6, doi: 10.1109/ICNAS.2019.8807875
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