A Powerful Face Preprocessing For Robust Kinship Verification based
  Tensor Analyses
Ammar chouchane Mohcene Bessaoudi Abdelmalik Ouamane
Kinship verification using facial photographs captured in the wild is
difficult area of research in the science of computer vision. It might be used
for a variety of applications, including image annotation and searching for
missing children, etc. The largest challenge to kinship verification in
practice is the fact that parent and child photos frequently differ
significantly from one another. How to effectively respond to such a challenge
is important improving the efficiency of kinship verification. For this
purpose, we introduce a system to check relatedness that starts with a pair of
face images of a child and a parent, after which it is revealed whether two
people are related or not. The first step in our approach is face preprocessing
with two methods, a Retinex filter and an ellipse mask, then a feature
extraction step based on hist-Gabor wavelets, which is used before an efficient
dimensionality reduction method called TXQDA. Finally, determine if there is a
relationship. By using Cornell KinFace benchmark database, we ran a number of
tests to show the efficacy of our strategy. Our findings show that, in
comparison to other strategies currently in use, our system is robust.