Facial kinship verification aims at automatically determining whether two
subjects have a kinship relation. It has been widely studied from different
modalities, such as faces, voices, gait, and smiling expressions. However, the
potential of bio-signals, such as remote Photoplethysmography (rPPG) extracted
from facial videos, remains largely unexplored in the kinship verification
problem. In this paper, we investigate for the first time the usage of the rPPG
signal for kinship verification. Specifically, we proposed a one-dimensional
Convolutional Neural Network (1DCNN) with a 1DCNN-Attention module and
contrastive loss to learn the kinship 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, the
proposed method is evaluated on the UvANEMO Smile Database from different kin
relations, showing the usefulness of rPPG signals in verifying kinship.

使用远程光电容积描记法（rPPG）信号进行面部亲缘关系验证的研究。提出了一种一维卷积神经网络（1DCNN）与 1DCNN-Attention 模块和对比损失相结合的方法，通过从不同 ROI 提取的多个 rPPG 信号为输入，学习亲缘相似性。最终，该方法在不同亲缘关系的 UvANEMO 微笑数据库上进行评估，展示了 rPPG 信号在验证亲缘关系中的实用性。