Kinship recognition: How far are we from viable solutions in smart environments?

Automated kinship recognition from face is a relatively recent problem that is mainly studied by the application of Deep Learning techniques. Despite the impact that an accurate kinship recognition algorithm can reach in a controlled environment, its applicability in smart environments is limited due to the degradation of performances. In this study we investigate the limitations of recent approaches that lead to a difficult applicability in a real case use. We present several tests on Siamese Neural Networks (SNN) based on a VGGFace architecture to solve both the kinship-vs-not-kinship recognition and the kind-of-kinship recognition. To perform our tests we used two popular kinship recognition Datasets that are Faces in the Wild and KinFace-II, respectively. To examine the behavior of the SNNs in a real scenario, we applied them, properly trained on the above mentioned datasets, to a popular TV show in which the aim is to discover kinship in a set of people. The weaknesses demonstrated in those tests have confirmed that the recent literature and algorithm to solve the kinship recognition problem are still far to achieve the high performances required in a smart environment.