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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine-learning system for adaptively changing a matching threshold in a biometric system, wherein the matching threshold is adapted to changing environmental conditions, the machine-learning system comprising: a batch aggregator device operable to: receive, via a communication interface, input data from the biometric system, wherein the input data comprises biometric data samples and environmental measures, wherein the environmental measures are data received from environmental sensors; and aggregate a batch of the received input data; a learning expert device operable to: compute a new suggestion for a matching threshold value for the biometric system based on the aggregated batch, wherein computing the new suggestion for the matching threshold value is performed using a first internal model, and wherein the first internal model comprises a first machine-learning algorithm; a second learning expert device operable to compute a second new suggestion for the matching threshold value for the biometric system based on the aggregated batch, wherein the second learning expert device uses a second internal model, wherein the second internal model comprises a second machine-learning algorithm different from the first machine-learning algorithm used by the first internal model; a comparison device operable to: compare the new suggestion for the matching threshold value with the second new suggestion for the matching threshold value, and generate a final suggestion for the matching threshold value based on the comparison; and an output device operable to output, via the communication interface, the final suggestion for the matching threshold value. 2. The machine-learning system of claim 1, wherein the input data includes one or more of: biometric data quality measures, wherein the biometric data quality measures relate to a quality of biometric data samples; biometric system performance measures including a False Acceptance Rate (FAR) and a False Rejection Rate (FRR); state indicators comprising a throughput and a time of a day; allowed limits of the biometric system comprising a minimum matching threshold value and a maximum FAR; or benchmark testing results, wherein the benchmark testing results are retrieved from a benchmark test performed on the biometric system before deployment. 3. The machine-learning system of claim 1, wherein, when aggregating the batch of the received input data, the batch aggregator device is to: collect statistics on matches performed by the biometric system; wherein, when collecting the statistics, the batch aggregator device is to: collect input data and output values of the matches performed by the biometric system during a batch window; and label the matches as true/false matches or true/false non-matches based on a ground truth. 4. The machine-learning system of claim 1, further comprising: a normalization device operable to: normalize the input data in the aggregated batch, and compute additional parameters from the normalized input data within the aggregated batch, wherein, when computing of the additional parameters, the normalization device is to at least one of: compute a mean of the normalized input data, compute a variance of the normalized input data; or compute a histogram of the normalized input data. 5. The machine-learning system of claim 1, wherein the machine-learning system is operable to: store the aggregated batch in a storage device; wherein the aggregated batch is re-sampled for continuous learning. 6. The machine-learning system of claim 1, wherein the first internal model is updated based on the aggregated batch using the first machine-learning algorithm; and wherein the second internal model is updated based on the aggregated batch using the second machine-learning algorithm different from the first machine-learning algorithm. 7. A computer-implemented method for adaptively changing a matching threshold in a biometric system using machine-learning, wherein the matching threshold is adapted to changing environmental conditions, the method comprising: receiving, at a batch aggregator device, input data from the biometric system via a communication interface, wherein the input data comprises biometric data samples and environmental measures, wherein the environmental measures are data received from environmental sensors; aggregating, at the batch aggregator device, a batch of the received input data; computing, at a learning expert device, a new suggestion for a matching threshold value of the biometric system based on the aggregated batch, wherein computing the new suggestion for the matching threshold value is performed using a first internal model, and wherein the first internal model comprises a first machine-learning algorithm; computing, at a second learning expert device, a second new suggestion for the matching threshold value of the biometric system based on the aggregated batch, wherein the second learning expert device uses a second internal model, wherein the second internal model comprises a second machine-learning algorithm different from the first machine-learning algorithm used by the first internal model; comparing, at a comparison device, the new suggestion for the matching threshold value with the second new suggestion for the matching threshold value to generate a final suggestion for the matching threshold value of the biometric system based on the comparison; and outputting, by an output device, the final suggestion for the matching threshold value of the biometric system via the communication interface. 8. The method of claim 7, wherein the input data includes one or more of: biometric data quality measures, wherein the biometric data quality measures relate to a quality of biometric data samples; biometric system performance measures including a False Acceptance Rate (FAR), and a False Rejection Rate (FRR); state indicators comprising a throughput and a time of a day; allowed limits of the biometric system comprising a minimum matching threshold value and a maximum FAR; or benchmark testing results, wherein the benchmark testing results are retrieved from a benchmark test performed on the biometric system before deployment. 9. The method of claim 7, wherein aggregating the batch of the received input data comprises: collecting statistics on matches performed by the biometric system; wherein the collecting statistics comprises: collecting input data and output values of the matches performed by the biometric system during a batch window; and labelling the matches as true/false matches or true/false non-matches based on a ground truth. 10. The method of claim 7, further comprising: normalizing, by a normalization device, the input data in the aggregated batch; and computing, by the normalization device, additional parameters from the normalized input data in the aggregated batch; and wherein computing the additional parameters from the data within the aggregated batch comprises one or more of: computing a mean of the normalized input data, computing a variance of the normalized input data, or computing a histogram of the normalized input data. 11. The method of claim 7, further comprising: storing the aggregated batch in a storage device, wherein the aggregated batch is re-sampled for continuous learning. 12. The method of claim 7, further comprising: updating the first internal model based on the aggregated batch using the first machine-learning algorithm; and updating the second internal model based on the aggregated batch using the second machine-learning algorithm different from the first machine-learning algorithm. 13. The method of claim 7, wherein the environmental measures includes at least one of: lighting conditions, humidity, or data related to demographics of users using the biometric system. 14. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by a device, cause the device to: receive input data from a biometric system via a communication interface, wherein the input data comprises biometric data samples and environmental measures, wherein the environmental measures are data received from environmental sensors; aggregate a batch of the received input data; compute a new suggestion for a matching threshold value of the biometric system based on the aggregated batch, wherein computing the new suggestion for the matching threshold value is performed using a first internal model, and wherein the first internal model comprises a first machine-learning algorithm; compute a second new suggestion for the matching threshold value of the biometric system based on the aggregated batch, wherein computing the second new suggestion uses a second internal model, wherein the second internal model comprises a second machine-learning algorithm different from the first machine-learning algorithm used by the first internal model; compare the new suggestion for the matching threshold value with the second new suggestion for the matching threshold value to generate a final suggestion for the matching threshold value of the biometric system based on the comparison; and output the final suggestion for the matching threshold value of the biometric system via the communication interface. 15. The non-transitory computer-readable medium of claim 14, wherein the input data includes one or more of: biometric data quality measures, wherein the biometric data quality measures relate to a quality of biometric data samples; biometric system performance measures including a False Acceptance Rate (FAR), and a False Rejection Rate (FRR); state indicators comprising a throughput and a time of a day; allowed limits of the biometric system comprising a minimum matching threshold value and a maximum FAR; or benchmark testing results, wherein the benchmark testing results are retrieved from a benchmark test performed on the biometric system before deployment. 16. The non-transitory computer-readable medium of claim 14, wherein the one or more instructions that cause the device to aggregate the batch of the received input data cause the device to: collect statistics on matches performed by the biometric system; wherein the one or more instructions that cause the device to collect the statistics cause the device to: collect input data and output values of the matches performed by the biometric system during a batch window; and label the matches as true/false matches or true/false non-matches based on a ground truth. 17. The non-transitory computer-readable medium of claim 14, wherein the instructions further comprise: one or more instructions that cause the device to normalize the input data in the aggregated batch; and one or more instructions that cause the device to compute additional parameters from the normalized input data in the aggregated batch; and wherein the one or more instructions to cause the device to compute the additional parameters from the data within the aggregated batch cause the device to one or more of: compute a mean of the normalized input data, compute a variance of the normalized input data, or compute a histogram of the normalized input data. 18. The non-transitory computer-readable medium of claim 14, wherein the instructions further comprise: one or more instructions that cause the device to store the aggregated batch in a storage device, wherein the aggregated batch is re-sampled for continuous learning. 19. The non-transitory computer-readable medium of claim 14, wherein the instructions further comprise: one or more instructions that cause the device to update the first internal model based on the aggregated batch using the first machine-learning algorithm; and one or more instructions that cause the device to update the second internal model based on the aggregated batch using the second machine-learning algorithm different from the first machine-learning algorithm. 20. The non-transitory computer-readable medium of claim 14, wherein the environmental measures includes at least one of: lighting conditions, humidity, or data related to demographics of users using the biometric system.