# Biometric Template Storage with Blockchain: A First Look into Cost and Performance Tradeoffs

arXiv: https://arxiv.org/abs/1904.13128 · HTML: https://mlsanity.com/paper/1904.13128/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [ICDAR 2021 Competition on On-Line Signature Verification](https://mlsanity.com/paper/2106.00739/) (2021)
- [OTB-morph: One-Time Biometrics via Morphing applied to Face Templates](https://mlsanity.com/paper/2111.13213/) (2021)
- [Introduction to Presentation Attacks in Signature Biometrics and Recent Advances](https://mlsanity.com/paper/2302.08320/) (2023)
- [Blockchain and Biometrics: Survey, GDPR Analysis, and Future Directions](https://mlsanity.com/paper/2302.10883/) (2023)
- [A secure and private ensemble matcher using multi-vault obfuscated templates](https://mlsanity.com/paper/2404.05205/) (2024)

## Abstract (arXiv)

We explore practical tradeoffs in blockchain-based biometric template storage. We first discuss opportunities and challenges in the integration of blockchain and biometrics, with emphasis in biometric template storage and protection, a key problem in biometrics still largely unsolved. Blockchain technologies provide excellent architectures and practical tools for securing and managing the sensitive and private data stored in biometric templates, but at a cost. We explore experimentally the key tradeoffs involved in that integration, namely: latency, processing time, economic cost, and biometric performance. We experimentally study those factors by implementing a smart contract on Ethereum for biometric template storage, whose cost-performance is evaluated by varying the complexity of state-of-the-art schemes for face and handwritten signature biometrics. We report our experiments using popular benchmarks in biometrics research, including deep learning approaches and databases captured in the wild. As a result, we experimentally show that straightforward schemes for data storage in blockchain (i.e., direct and hash-based) may be prohibitive for biometric template storage using state-of-the-art biometric methods. A good cost-performance tradeoff is shown by using a blockchain approach based on Merkle trees.