Interactive user authentication

1. A computer-implemented method for verifying an electronic device user, the method comprising the steps of:

issuing at least one action instruction comprising a liveness challenge to an electronic device user using a notification mechanism of the electronic device;

recording response data from a plurality of data acquisition systems of the electronic device, the response data pertaining to the user's response to the at least one action instruction;

processing the response data to form classification scores, including identifying, from the response data, the likelihood of at least one characteristic pattern associated with an action instruction, comprising:

processing video data to assess a classification score of at least one characteristic motion associated with the action instruction by:

performing a plurality of head pose estimations on the video data,

processing the plurality of head pose estimations to form extracted pose information, including: extracting a series of angles from the plurality of head pose estimations; fitting a function to the series of angles; constructing a feature vector from parameters of the function, the fitting of the function, and the head pose estimations; and testing if the video data contains at least one characteristic motion with the feature vector, and

forming a facial action classification score using the extracted pose information;

combining the classification scores to form a classification value; and

verifying the user when the classification value satisfies a threshold.

2. The method of claim 1 , wherein at least one action instruction comprises an audio liveness challenge and a motion liveness challenge.

3. The method of claim 1 , wherein the plurality of data acquisition systems comprises a first data acquisition system and a second data acquisition system, wherein the first data acquisition system is different from the second data acquisition system.

4. The method of claim 1 , wherein the step of processing the response data to form classification scores comprises assessing a quality score for at least one data type.

5. The method of claim 4 , wherein the at least one data type comprises video data and, wherein assessing a quality score for video data takes into account at least one of:

frame resolution; video frame rate; colour balance; contrast; illumination; blurriness; presence of a face; and glare.

6. The method of claim 4 , wherein the at least one data type comprises audio data, and wherein assessing a quality score of audio data takes into account at least one of: sampling frequency; background noise; and quality of the sound recording equipment.

7. The method of claim 1 , wherein combining the classification scores to form a classification value comprises weighting each classification score by a quality assessment relating to a relevant data type.

8. The method of claim 7 , wherein weighting each classification score by the quality assessment relating to the relevant data type comprises:

weighting a classification score relating to video data with a quality score for video data; and

weighting a classification score relating to audio data with a quality score for audio data.

9. A non-transitory computer-readable medium comprising executable instructions for performing the method of claim 1 .

10. A computer comprising a processor configured to execute executable code stored in memory, wherein the executable code comprises instructions for performing the method of claim 1 .

11. The method of claim 1 , wherein processing video data to assess the classification score of at least one characteristic motion associated with the action instruction comprises performing visual speech recognition on the video data.