Kinship recognition from faces using deep learning with imbalanced data

Kinship verification from faces aims to determine whether two person share some family relationship based only on the visual facial patterns. This has attracted a significant interests among the scientific community due to its potential applications in social media mining and finding missing children. In this work, We propose a novel pattern analysis technique for kinship verification based on a new deep learning-based approach. More specifically, given a pair of face images, we first use Resnet50 to extract deep features from each image. Then, feature distances between each pair of images are computed. Importantly, to overcome the problem of unbalanced data, One Hot Encoding for labels is utilised. The distances finally are fed to a deep neural networks to determine the kinship relation. Extensive experiments are conducted on FIW dataset containing 11 classes of kinship relationships. The experiments showed very promising results and pointed out the importance of balancing the training dataset. Moreover, our approach showed interesting ability of generalization. Results show that our approach performs better than all existing approaches on grandparents-grandchildren type of kinship. To support the principle of open and reproducible research, we are soon making our code publicly available to the research community: github.com/Steven-HDQ/Kinship-Recognition.