aiqbench analysis
This research advances kinship verification by leveraging graph neural networks to improve accuracy over traditional face representation methods, achieving state-of-the-art results on standard benchmarks like KinFaceW-II. The study demonstrates that integrating specific loss functions and classification module structures can significantly enhance performance in computer vision tasks focused on familial relationships. While technically grounded in machine learning, the application remains a niche within broader AI research rather than addressing general-purpose models or societal impacts.
Kinship Verification through a Forest Neural Network
arXiv:2504.18910v2 Announce Type: replace-cross Abstract: 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
Abstract: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 this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2504.18910 [cs.CV]
(or arXiv:2504.18910v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2504.18910 Focus to learn more arXiv-issued DOI via DataCite
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