  Kinship verification is a long-standing research challenge in computer
vision. The visual differences presented to the face have a significant effect
on the recognition capabilities of the kinship systems. We argue that
aggregating multiple visual knowledge can better describe the characteristics
of the subject for precise kinship identification. Typically, the age-invariant
features can represent more natural facial details. Such age-related
transformations are essential for face recognition due to the biological
effects of aging. However, the existing methods mainly focus on employing the
single-view image features for kinship identification, while more meaningful
visual properties such as race and age are directly ignored in the feature
learning step. To this end, we propose a novel deep collaborative multi-modal
learning (DCML) to integrate the underlying information presented in facial
properties in an adaptive manner to strengthen the facial details for effective
unsupervised kinship verification. Specifically, we construct a well-designed
adaptive feature fusion mechanism, which can jointly leverage the complementary
properties from different visual perspectives to produce composite features and
draw greater attention to the most informative components of spatial feature
maps. Particularly, an adaptive weighting strategy is developed based on a
novel attention mechanism, which can enhance the dependencies between different
properties by decreasing the information redundancy in channels in a
self-adaptive manner. To validate the effectiveness of the proposed method,
extensive experimental evaluations conducted on four widely-used datasets show
that our DCML method is always superior to some state-of-the-art kinship
verification methods.
