  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.
