University of Oulu

O. Laiadi, A. Ouamane, A. Benakcha, A. Taleb-Ahmed and A. Hadid, "Multi-view Deep Features for Robust Facial Kinship Verification," 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), Buenos Aires, Argentina, 2020, pp. 877-881, doi: 10.1109/FG47880.2020.00118

Multi-view deep features for robust facial kinship verification

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Author: Laiadi, Oualid1,2; Ouamane, Abdelmalik3; Benakcha, Abdelhamid4;
Organizations: 1Laboratory of LESIA, University of Biskra, Algeria
2Univ. Polytechnique Hauts-de-France, CNRS, Univ. Lille, ISEN, Centrale Lille, UMR 8520 - IEMN - DOAE, F-59313 Valenciennes, France
3Laboratory of LI3C, University of Biskra, Algeria
4Laboratory of LGEB, University of Biskra, Algeria
5Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-03-25


Automatic kinship verification from facial images is an emerging research topic in machine learning community. In this paper, we proposed an effective facial features extraction model based on multi-view deep features. Thus, we used four pre-trained deep learning models using eight features layers (FC6 and FC7 layers of each VGG-F, VGG-M, VGG-S and VGG-Face models) to train the proposed Multilinear Side-Information based Discriminant Analysis integrating Within Class Covariance Normalization (MSIDA + WCCN) method. Furthermore, we show that how can metric learning methods based on WCCN method integration improves the Simple Scoring Cosine similarity (SSC) method. We refer that we used the SSC method in RFIW’20 competition using the eight deep features concatenation. Thus, the integration of WCCN in the metric learning methods decreases the intra-class variations effect introduced by the deep features weights. We evaluate our proposed method on two kinship benchmarks namely KinFaceW-I and KinFaceW-II databases using four Parent-Child relations (Father-Son, Father-Daughter, Mother-Son and Mother-Daughter). Thus, the proposed MSIDA + WCCN method improves the SSC method with 12.80% and 14.65% on KinFaceW-I and KinFaceW-II databases, respectively. The results obtained are positively compared with some modern methods, including those that rely on deep learning.

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ISBN: 978-1-7281-3079-8
ISBN Print: 978-1-7281-3080-4
Pages: 877 - 881
DOI: 10.1109/FG47880.2020.00118
Host publication: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
Conference: IEEE International Conference on Automatic Face and Gesture Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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