  Facial Kinship Verification (FKV) aims at automatically determining whether
two subjects have a kinship relation based on human faces. It has potential
applications in finding missing children and social media analysis. Traditional
FKV faces challenges as it is vulnerable to spoof attacks and raises privacy
issues. In this paper, we explore for the first time the FKV with vital
bio-signals, focusing on remote Photoplethysmography (rPPG). rPPG signals are
extracted from facial videos, resulting in a one-dimensional signal that
measures the changes in visible light reflection emitted to and detected from
the skin caused by the heartbeat. Specifically, in this paper, we employed a
straightforward one-dimensional Convolutional Neural Network (1DCNN) with a
1DCNN-Attention module and kinship contrastive loss to learn the kin similarity
from rPPGs. The network takes multiple rPPG signals extracted from various
facial Regions of Interest (ROIs) as inputs. Additionally, the 1DCNN attention
module is designed to learn and capture the discriminative kin features from
feature embeddings. Finally, we demonstrate the feasibility of rPPG to detect
kinship with the experiment evaluation on the UvANEMO Smile Database from
different kin relations.
