Biometric user authentication
One aspect provides a method of authenticating a user of a user device, the method comprising: receiving motion data captured using a motion sensor of the user device during an interval of motion of the user device induced by the user; processing the motion data to generate a device motion feature vector, inputting the device motion feature vector to a neural network, the neural network having been trained to distinguish between device motion feature vectors captured from different users; and authenticating the user of the user device, by using a resulting vector output of the neural network to determine whether the user-induced device motion matches an expected device motion pattern uniquely associated with an authorized user, the neural network having been trained based on device motion feature vectors captured from a group of training users, which does not include the authorized user.
1. One or more non-transitory computer-readable media comprising computer-readable instructions to authenticate a user of a hand-held user device, wherein the computer-readable instructions, when executed on one or more hardware processors, cause the one or more hardware processors to implement operations comprising:
outputting at the hand-held user device instructions requesting the user to induce motion of the hand-held user device for authenticating the user;
receiving motion data captured using a motion sensor of the hand-held user device during an interval of motion of the user device induced by the user whilst holding the hand-held user device;
processing the motion data to generate a device motion feature vector;
receiving image data captured using an image capture device of the user device during a time of the user-induced device motion;
inputting the device motion feature vector to a neural network, the neural network having been trained to distinguish between device motion feature vectors captured from different users; and
authenticating the user of the user device by using a resulting vector output of the neural network to determine whether the user-induced device motion matches an expected device motion pattern uniquely associated with an authorized user, the neural network having been trained based on device motion feature vectors captured from a group of training users, which does not include the authorized user;
wherein authenticating the user of the user device comprises analyzing the image data to determine whether three-dimensional facial structure is present therein; and
wherein the motion data comprises a time series of device motion values, and wherein authenticating the user of the user device comprises comparing the image data with at least some of the device motion values, to verify that movement of the three-dimensional facial structure, if present, corresponds to the user-induced device motion.
2. The one or more non-transitory computer-readable media of claim 1 , wherein authenticating the user comprises classifying, by a classifier, the vector output of the neutral network as matching or not matching the expected device motion pattern.
3. The one or more non-transitory computer-readable media of claim 2 , wherein the classifier has been trained for classifying the vector output of the neural network based on one or more earlier device motion feature vectors captured from the authorized user and corresponding to the expected device motion pattern.
4. The one or more non-transitory computer-readable media of claim 3 , wherein the classifier is a one-class classifier trained without using any data captured from any unauthorized user.
5. The one or more non-transitory computer-readable media of claim 3 , wherein the classifier is a binary classifier trained using one or more earlier device motion feature vectors captured from at least one unauthorized user.
6. The one or more non-transitory computer-readable media of claim 3 , wherein the classifier is a support vector machine (SVM).
7. The one or more non-transitory computer-readable media of claim 1 , wherein the computer-readable instructions, when executed, further cause the one or more hardware processors to, when the user-induced device motion is determined to match the expected device motion pattern, grant the user of the user device access to at least one of: a function of the user device, a service, and data to which the authorized user is permitted access.
8. The one or more non-transitory computer-readable media of claim 1 , wherein the vector output is an output of a hidden layer of the neural network.
9. The one or more non-transitory computer-readable media of claim 1 , wherein the neural network is a convolutional neural network (CNN).
10. The one or more non-transitory computer-readable media of claim 1 , wherein the device motion feature vector comprises temporal motion values of or derived from the motion data.
11. The one or more non-transitory computer-readable media of claim 1 , wherein the motion data comprises a time series of device motion values, and the operations further comprise applying a domain transform to at least a portion of the time series to determine at least one device motion spectrum in the frequency domain, wherein the device motion feature vector comprises coefficients of the said at least one device motion spectrum or values derived from coefficients of the said at least one device motion spectrum.
12. The one or more non-transitory computer-readable media of claim 11 wherein the device motion feature vector comprises logarithmic values and/or cepstral coefficients determined from the device motion spectrum.
13. The one or more non-transitory computer-readable media of claim 12 , wherein multiple device motion spectra are determined, each by applying a domain transform to a respective portion of the time series of device motion values, and analyzed to determine whether the user-induced motion of the user device matches the expected device motion pattern.
14. One or more non-transitory computer-readable media comprising computer-readable instructions to authenticate a user of a user device, wherein the computer-readable instructions, when executed on one or more hardware processors, cause the one or more hardware processors to implement operations comprising:
receiving motion data captured using a motion sensor of the user device during an interval of motion of the user device induced by the user;
processing the motion data to generate a device motion feature vector;
inputting the device motion feature vector to a neural network, the neural network having been trained to distinguish between device motion feature vectors captured from different users; and
authenticating the user of the user device, by using a resulting vector output of the neural network to determine whether the user-induced device motion matches an expected device motion pattern uniquely associated with an authorized user, the neural network having been trained based on device motion feature vectors captured from a group of training users, which does not include the authorized user;
wherein the motion data comprises a time series of device motion values, and the operations comprise applying a domain transform to at least a portion of the time series to determine at least one device motion spectrum in the frequency domain, and wherein the device motion feature vector comprises coefficients of the said at least one device motion spectrum or values derived from coefficients of the said at least one device motion.
15. The one or more non-transitory computer-readable media of claim 14 , wherein the user of the device is authenticated based on analysis of the motion data in combination with facial recognition.
16. The one or more non-transitory computer-readable media of claim 14 , wherein authenticating the user of the user device further comprises comparing image data with the motion data, to verify that movement of a three-dimensional facial structure, if present within the image data, corresponds to the user-induced device motion.
17. A user authentication system for authenticating a user of a user device, the user authentication system comprising:
an input configured to receive motion data captured using a motion sensor of the user device during an interval of motion of the user device induced by the user; and
one or more hardware processors configured to execute computer readable instructions, which, when executed, cause the one or more processors to implement operations comprising:
receiving motion data captured using a motion sensor of the user device during an interval of motion of the user device induced by the user;
processing the motion data to generate a device motion feature vector,
inputting the device motion feature vector to a neural network, the neural network having been trained to distinguish between device motion feature vectors captured from different users; and
authenticating the user of the user device, by using a resulting vector output of the neural network to determine whether the user-induced device motion matches an expected device motion pattern uniquely associated with an authorized user, the neural network having been trained based on device motion feature vectors captured from a group of training users, which does not include the authorized user;
wherein the motion data comprises a time series of device motion values, and the operations comprise applying a domain transform to at least a portion of the time series to determine at least one device motion spectrum in the frequency domain, wherein the device motion feature vector comprises coefficients of the said at least one device motion spectrum or values derived from coefficients of the said at least one device motion spectrum.