  Kinship verification from facial images has been recognized as an emerging
yet challenging technique in many potential computer vision applications. In
this paper, we propose a novel cross-generation feature interaction learning
(CFIL) framework for robust kinship verification. Particularly, an effective
collaborative weighting strategy is constructed to explore the characteristics
of cross-generation relations by corporately extracting features of both
parents and children image pairs. Specifically, we take parents and children as
a whole to extract the expressive local and non-local features. Different from
the traditional works measuring similarity by distance, we interpolate the
similarity calculations as the interior auxiliary weights into the deep CNN
architecture to learn the whole and natural features. These similarity weights
not only involve corresponding single points but also excavate the multiple
relationships cross points, where local and non-local features are calculated
by using these two kinds of distance measurements. Importantly, instead of
separately conducting similarity computation and feature extraction, we
integrate similarity learning and feature extraction into one unified learning
process. The integrated representations deduced from local and non-local
features can comprehensively express the informative semantics embedded in
images and preserve abundant correlation knowledge from image pairs. Extensive
experiments demonstrate the efficiency and superiority of the proposed model
compared to some state-of-the-art kinship verification methods.
