Verifying Kinship from RGB-D Face Data

Abstract

We present a kinship verification (KV) approach based on Deep Learning applied to RGB-D facial data. To work around the lack of an adequate 3D face database with kinship annotations, we provide an online platform where participants upload videos containing faces of theirs and of their relatives. These videos are captured with ordinary smartphone cameras. We process them to reconstruct recorded faces in tridimensional space, generating a normalized dataset which we call Kin3D. We also combine depth information from the normalized 3D reconstructions with 2D images, composing a set of RGBD data. Following approaches from related works, images are organized into four categories according to their respective type of kinship. For the classification, we use a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM) for comparison. The CNN was tested both on a widely used 2D Kinship Verification database (KinFaceW-I and II) and on our Kin3D for comparison with related works. Results indicate that adding depth information improves the model’s performance, increasing the classification accuracy up to 90%. To the extent of our knowledge, this is the first database containing depth information for Kinship Verification. We provide a baseline performance to stimulate further evaluations from the research community.