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.

该研究通过人类研究和基于过滤器和压缩正则化惩罚的新颖特征表示方法（fcDBN）提出一种层次化的亲属验证方法，并使用 WVU 亲属数据库验证其在人脸验证中的性能提升。