Kinship verification is a very important technique in many real-world applications, e.g., personal album organization, missing person investigation and forensic analysis. However, it is extremely difficult to verify a family pair with generation gap, e.g., father and son, since there exist both age gap and identity variation. It is essential to well fight off such challenges to achieve promising kinship verification performance. To this end, we propose a towards-young cross-generation model for effective kinship verification by mitigating both age and identity divergences. Specifically, we explore a conditional generative model to bring in an intermediate domain to bridge each pair. Thus, we could extract more effective features through deep architectures with a newly-designed Sparse Discriminative Metric Loss (SDM-Loss), which is exploited to involve the positive and negative information. Experimental results on kinship benchmark demonstrate the superiority of our proposed model by comparing with the state-of-the-art kinship verification methods.

中文翻译：

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具有稀疏判别度量的跨代亲属关系验证


亲属关系验证是许多现实应用中非常重要的技术，例如个人相册组织、失踪人员调查和法医分析。然而，验证具有代沟的家庭配对，例如父亲和儿子，是极其困难的，因为既存在年龄差距，又存在身份变异。必须克服这些挑战才能实现有希望的亲属关系验证性能。为此，我们提出了一种面向年轻的跨代模型，通过减少年龄和身份差异来进行有效的亲属关系验证。具体来说，我们探索了一个条件生成模型，引入一个中间域来桥接每一对。因此，我们可以通过新设计的稀疏判别度量损失（SDM-Loss）的深层架构来提取更有效的特征，该损失被用来涉及正面和负面信息。通过与最先进的亲属关系验证方法进行比较，亲属关系基准的实验结果证明了我们提出的模型的优越性。