  Kinship verification has a number of applications such as organizing large
collections of images and recognizing resemblances among humans. In this
research, first, a human study is conducted to understand the capabilities of
human mind and to identify the discriminatory areas of a face that facilitate
kinship-cues. Utilizing the information obtained from the human study, a
hierarchical Kinship Verification via Representation Learning (KVRL) framework
is utilized to learn the representation of different face regions in an
unsupervised manner. We propose a novel approach for feature representation
termed as filtered contractive deep belief networks (fcDBN). The proposed
feature representation encodes relational information present in images using
filters and contractive regularization penalty. A compact representation of
facial images of kin is extracted as an output from the learned model and a
multi-layer neural network is utilized to verify the kin accurately. A new WVU
Kinship Database is created which consists of multiple images per subject to
facilitate kinship verification. The results show that the proposed deep
learning framework (KVRL-fcDBN) yields stateof-the-art kinship verification
accuracy on the WVU Kinship database and on four existing benchmark datasets.
Further, kinship information is used as a soft biometric modality to boost the
performance of face verification via product of likelihood ratio and support
vector machine based approaches. Using the proposed KVRL-fcDBN framework, an
improvement of over 20% is observed in the performance of face verification.
