Kinship Verification through a Forest Neural Network
This work addresses kinship verification, a domain-specific problem in computer vision, with incremental improvements over existing methods.
The paper tackled kinship verification by proposing a forest neural network that uses graph neural network concepts to improve face representation accuracy, achieving the best result on KinFaceW-II with an average improvement of nearly 1.6 for all kinship types and near-best performance on KinFaceW-I.
Early methods used face representations in kinship verification, which are less accurate than joint representations of parents' and children's facial images learned from scratch. We propose an approach featuring graph neural network concepts to utilize face representations and have comparable results to joint representation algorithms. Moreover, we designed the structure of the classification module and introduced a new combination of losses to engage the center loss gradually in training our network. Additionally, we conducted experiments on KinFaceW-I and II, demonstrating the effectiveness of our approach. We achieved the best result on KinFaceW-II, an average improvement of nearly 1.6 for all kinship types, and we were near the best on KinFaceW-I. The code is available at https://github.com/ali-nazari/Kinship-Verification