  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.
