This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors.

中文翻译：

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区块链上具有分层可导航小世界的去中心化人脸识别

本文提出了一种使用分层可导航小世界（HNSW）算法在基于深度学习的人脸识别系统中进行分散存储的新方法。所提出的解决方案利用以太坊智能合约，该合约充当高度可用的数据存储系统，用于为授权人员存储可识别数据。此外，该解决方案还与集中式矢量数据库集成，该数据库负责矢量索引、搜索并将人脸嵌入与具有匿名哈希值的以太坊区块链上的身份相关联。向量索引和搜索过程涉及不同的机器学习算法，这些算法使得计算能够在合理的时间内以良好的匹配精度进行。具体来说，我们比较了不同的方法并选择了 HNSW 算法。因此，我们成功地为海关检查场所等政府监控区域实施了可靠且注重隐私的去中心化人脸识别系统原型。在我们的测量中，该系统可以轻松处理 20,000 个人脸向量，并且匹配精度很高，并且使用更强大的硬件可以进一步提高性能。最后，我们还提出了其他方法来进一步扩展系统以处理数百万个面部向量。