A Unified Approach to Kinship Verification

• URL: http://arxiv.org/abs/2009.05871v1
• Date: Sat, 12 Sep 2020 22:13:56 GMT
• Title: A Unified Approach to Kinship Verification
• Authors: Eran Dahan and Yosi Keller
• Abstract summary: We propose a deep learning-based approach for kin verification using a unified multi-task learning scheme. We introduce a novel approach for fusing the embeddings of kin images, to avoid overfitting, which is a common issue in training such networks. An adaptive sampling scheme is derived for the training set images to resolve the inherent imbalance in kin verification datasets.
• Score: 10.508187462682308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a deep learning-based approach for kin verification using a unified multi-task learning scheme where all kinship classes are jointly learned. This allows us to better utilize small training sets that are typical of kin verification. We introduce a novel approach for fusing the embeddings of kin images, to avoid overfitting, which is a common issue in training such networks. An adaptive sampling scheme is derived for the training set images to resolve the inherent imbalance in kin verification datasets. A thorough ablation study exemplifies the effectivity of our approach, which is experimentally shown to outperform contemporary state-of-the-art kin verification results when applied to the Families In the Wild, FG2018, and FG2020 datasets.

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