  One of the unsolved challenges in the field of biometrics and face
recognition is Kinship Verification. This problem aims to understand if two
people are family-related and how (sisters, brothers, etc.) Solving this
problem can give rise to varied tasks and applications. In the area of homeland
security (HLS) it is crucial to auto-detect if the person questioned is related
to a wanted suspect, In the field of biometrics, kinship-verification can help
to discriminate between families by photos and in the field of predicting or
fashion it can help to predict an older or younger model of people faces.
Lately, and with the advanced deep learning technology, this problem has gained
focus from the research community in matters of data and research. In this
article, we propose using a Deep Learning approach for solving the
Kinship-Verification problem. Further, we offer a novel self-learning deep
model, which learns the essential features from different faces. We show that
our model wins the Recognize Families In the Wild(RFIW2018,FG2018) challenge
and obtains state-of-the-art results. Moreover, we show that our proposed model
can reduce the size of the network by half without loss in performance.
