Kinship Identification Through Joint Learning Using Kinship Verification Ensembles

Wei Wang

Abstract:

Kinship verification is a well-explored task: identifying whether or not two persons are kin. In contrast, kinship identification
has been largely ignored so far. Kinship identification aims to further
identify the particular type of kinship. An extension to kinship verification run short to properly obtain identification, because existing verification networks are individually trained on specific kinships and do
not consider the context between different kinship types. Also, existing
kinship verification datasets have biased positive-negative distributions
which are different than real-world distributions.
To this end, we propose a novel kinship identification approach based
on joint training of kinship verification ensembles and classification modules. We propose to rebalance the training dataset to become more realistic. Large scale experiments demonstrate the appealing performance
on kinship identification. The experiments further show significant performance improvement of kinship verification when trained on the same
dataset with more realistic distributions.