Biometric system

1. A biometric authentication system, comprising:

headwear including a plurality of biosensors each configured to sample muscle activity so as to obtain a respective time-varying signal, wherein the biosensors are located on the headwear such that, in use when worn by a user, the biosensors are configured to detect muscle activity of a predetermined plurality of facial muscles comprising corresponding muscles from each side of the user's face;

a data store for storing a data set representing characteristic muscle activity for one or more users; and

a processor configured to:

process the time-varying signals from the biosensors to determine a measure of asymmetry in the muscle activity of the corresponding muscles from each side of the user's face;

process the time-varying signals from the biosensors in dependence on the stored data set so as to determine a correspondence between a time-varying signal and characteristic muscle activity of one of the one or more users, and

in dependence on the determined correspondence and the determined measure of asymmetry, authenticate the time-varying signals as being associated with the user.

2. The biometric authentication system according to claim 1 , wherein the predetermined plurality of facial muscles includes a muscle that is involuntarily co-contracted in at least one facial expression.

3. The biometric authentication system according to claim 1 , wherein the data set includes a stored time-varying signal, and the processor is configured to process the time-varying signals in dependence on the stored data set by comparing the time-varying signals to the stored time-varying signal.

4. A biometric authentication system according to claim 3 , wherein the processor is configured to compare the time-varying signals to the stored time-varying signal by determining whether the signals are within a predetermined tolerance of one another.

5. The biometric authentication system according to claim 1 , wherein the data set includes one or more stored signal features, and the processor is configured to process the time-varying signals in dependence on the stored data set by comparing one or more signal features of the time-varying signals to the one or more stored signal features.

6. The biometric authentication system according to claim 5 , wherein the processor is configured to determine whether a threshold number of signal features of the time-varying signals correspond to the one or more stored signal features.

7. A biometric authentication system according to claim 5 , wherein the processor is configured to determine that a signal feature of the time-varying signals corresponds to a stored signal feature where the respective signal features are within a predetermined tolerance of one another.

8. A biometric authentication system according to claim 5 , wherein the processor is configured to determine that a signal feature of the time-varying signals corresponds to a stored signal feature where the signal feature exceeds a threshold value for that signal feature determined in dependence on the stored signal feature.

9. A biometric authentication system according to claim 1 , wherein the characteristic muscle activity represented by the data set includes a measure of at least one selected from the group consisting of:

an amplitude of at least one of the time-varying signals;

a variation with time of the amplitude of at least one of the time-varying signals;

a measure of asymmetry in the muscle activity of corresponding muscles from each side of the user's face; and

the timing of activation of at least one facial muscle, relative to a common time base or to the timing of activation of at least one other facial muscle.

10. A biometric authentication system according to claim 1 , wherein the processor is configured to determine the correspondence between the time-varying signals and the characteristic muscle activity by at least one selected from the group consisting of:

applying a pattern recognition algorithm; and

applying an analysis of variance test.

11. A biometric authentication system according to claim 1 , wherein the processor is configured to process the time-varying signals to obtain a plurality of representative values of the time-varying signals, the data set including a plurality of stored representative values, the representative values and the stored representative values being generated by a predefined algorithm, wherein the processor is configured to compare the representative values to the stored representative values.

12. A biometric authentication system according to claim 1 , wherein one or more of the biosensors are electric potential sensors.

13. A biometric authentication system according to claim 1 , wherein the headwear includes sensors for determining the user's gaze.

14. A biometric authentication system according to claim 1 , wherein the processor is configured to process the time-varying signal by at least one selected from the group consisting of:

clipping the signal;

signal denoising;

applying a signal baseline correction;

using onset detection;

using data segmentation;

applying a log-transform; and

extracting one or more features from the signal.

15. A biometric authentication system according to claim 1 , wherein the sampled muscle activity is electrical muscle activity and the stored data set comprises characteristic electrical muscle activity for the one or more users.

16. A biometric authentication system according to claim 1 , wherein the biometric authentication system is configured to output an authentication signal for controlling access to a resource.

17. A method for authenticating a user wearing headwear, the headwear including a plurality of biosensors configured to sample muscle activity comprising corresponding muscles from each side of the user's face, the method comprising:

sampling, using each of the plurality of biosensors, muscle activity of the user to obtain a respective time-varying signal;

processing the time-varying signals from the biosensors to determine a measure of asymmetry in the muscle activity of the corresponding muscles from each side of the user's face;

processing the time-varying signals from the biosensors in dependence on a data set representing characteristic muscle activity for one or more users so as to determine a correspondence between a time-varying signal and characteristic muscle activity of one of the one or more users; and

in dependence on the determined correspondence and the determined measure of asymmetry, authenticating the time-varying signals as being associated with the user.