Abstract: In some implementations, a system may generate information that identifies a passphrase to be used as a biometric input. The system may receive a voice input of a user speaking the passphrase. The system may generate one or more cryptographic keys based on the voice input. The system may generate a digital identifier based on the one or more cryptographic keys. The system may generate one or more biometric templates for the user. The system may encrypt the one or more biometric templates using the one or more cryptographic keys and to generate one or more encrypted biometric templates. The system may store in a secure storage associated with the user, at least one of the digital identifier, a public key of the one or more cryptographic keys, a phone number associated with the user, or the one or more encrypted biometric templates. Numerous other aspects are provided.

Abstract: Summarizing, the application relates to a machine-learning system for adaptively changing a matching threshold of a biometric system. The machine-learning system comprises a batch aggregator device operable to receive input data from the biometric system via a communication interface and to aggregate a batch of at least some of the received input data. The machine-learning system further comprises a learning expert device operable to compute a new suggestion for a matching threshold value of the biometric system based on the aggregated batch. Finally, the machine-learning system comprises an output device operable to output the computed new suggestion for the matching threshold of the biometric system via the communication interface.

Abstract: Summarizing, the application relates to a machine-learning system for adaptively changing a matching threshold of a biometric system. The machine-learning system comprises a batch aggregator device operable to receive input data from the biometric system via a communication interface and to aggregate a batch of at least some of the received input data. The machine-learning system further comprises a learning expert device operable to compute a new suggestion for a matching threshold value of the biometric system based on the aggregated batch. Finally, the machine-learning system comprises an output device operable to output the computed new suggestion for the matching threshold of the biometric system via the communication interface.