BACKGROUND OF THE INVENTION

This invention relates generally to facial images captured during an authentication transaction, and more particularly, enhancing liveness detection of facial image data.

Users conduct transactions with many different service providers in person and remotely over the Internet. Network-based transactions conducted over the Internet may involve purchasing items from a merchant website or accessing confidential information from a website. Service providers that own and operate such websites typically require successfully identifying users before allowing a desired transaction to be conducted. For service providers who require biometric authentication, a claim of identity and remotely captured data regarding a biometric modality are provided. However, imposters have been known to impersonate others by providing a false claim of identity supported by fraudulent data in an effort to deceive an entity into concluding the imposter is the person he or she claims to be. Such impersonations are known as spoofing.

Impostors have been known to use many methods to obtain or create fraudulent data for a biometric modality of another person that can be submitted during biometric authentication transactions. For example, imposters have been known to obtain two-dimensional pictures from social networking sites which can be presented to a camera during authentication to support a false claim of identity. Imposters have also been known to make physical models of a biometric modality, such as a fingerprint using gelatin or a three-dimensional face using a custom mannequin. Moreover, imposters have been known to eavesdrop on networks during legitimate network-based biometric authentication transactions to surreptitiously obtain genuine data of a biometric modality of a person. The imposters use the obtained data for playback during fraudulent network-based authentication transactions. Such fraudulent data are difficult to detect using known liveness detection methods. Consequently, generating accurate network-based biometric authentication transaction results with data for a biometric modality captured from a person at a remote location depends on verifying the physical presence of the person during the authentication transaction as well as accurately verifying the identity of the person with the captured data. Verifying that the data for a biometric modality of a person captured during a network-based biometric authentication transaction conducted at a remote location is of a live person is known as liveness detection or anti-spoofing.

Liveness detection methods have been known to use structure derived from motion of a biometric modality, such as a person's face, to distinguish a live person from a photograph. Other methods have been known to analyze sequential images of eyes to detect eye blinks and thus determine if an image of a face is from a live person. Yet other methods have been known to illuminate a biometric modality with a pattern to distinguish a live person from a photograph. However, these methods may not be considered to be convenient and may not accurately detect spoofing. As a result, these methods may not provide high confidence liveness detection support for service providers dependent upon accurate biometric authentication transaction results.

BRIEF DESCRIPTION OF THE INVENTION

An aspect of the present disclosure provides a method for enhanced liveness detection of facial image data that includes capturing movement data of an electronic device while capturing, using the electronic device, facial image data of a user. In response to determining the captured movement data is consistent with movement data expected to be captured during capture of facial image data, the method includes deciding the captured facial image data is genuine. In response to determining the captured movement data is different than movement data expected to be generated during capture of facial image data, the method includes deciding the captured facial image data is fraudulent.

In another aspect of the present disclosure, the step of determining the captured movement data is consistent with movement data expected to be captured during capture of facial image data includes inputting the captured movement data into a pre-trained machine learning algorithm and generating a confidence score. The confidence score is compared against a threshold score, and the captured facial image data is decided to be genuine when the confidence score is at least equal to the threshold score.

In another aspect of the present disclosure, the method includes transmitting a message to a service provider computer system indicating the captured facial image data is genuine.

In another aspect of the present disclosure, the step of determining the captured movement data is consistent with movement data expected to be captured during capture of facial image data includes calculating an angle of rotation of the electronic device about an axis of the electronic device, comparing the calculated angle against a threshold angle, and deciding the captured facial image data is a replay when the calculated angle is greater than the threshold angle.

In another aspect of the present disclosure, the step of determining the captured movement data is different than movement data expected to be captured during capture of facial image data comprises determining the facial image data is a replay.

An aspect of the present disclosure provides an electronic device for enhanced liveness detection of facial image data that includes a processor and a memory configured to store data. The electronic device is associated with a network and the memory is in communication with the processor and has instructions stored thereon. The instructions when read and executed by the processor, cause the electronic device to capture movement data of the electronic device while capturing, using the electronic device, facial image data of a user. Moreover, the instructions when read and executed by the processor cause the electronic device to decide the captured facial image data is genuine in response to determining the captured movement data is consistent with movement data expected to be captured during capture of facial image data. Furthermore, the instructions when read and executed by the processor cause the electronic device to decide the captured facial image data is fraudulent in response to determining the captured movement data is different than movement data expected to be generated during capture of facial image data.

In another aspect of the present disclosure, the instructions executed by the processor that cause the electronic device to determine the captured movement data is consistent with movement data expected to be captured during capture of facial image data also cause the electronic device to generate, by inputting the captured movement data into a pre-trained machine learning algorithm, a confidence score from the captured movement data. The confidence score is compared against a threshold score, and the captured facial image data is decided to be genuine when the confidence score is at least equal to the threshold score.

In another aspect of the present disclosure, the instructions, when read and executed by the processor further cause the electronic device to transmit a message to a service provider computer system indicating the captured facial image data is genuine.

In another aspect of the present disclosure, the instructions, when read and executed by the processor to cause the electronic device to determine the captured movement data is consistent with movement data expected to be captured during capture of facial image data, further cause the electronic device to calculate an angle of rotation of the electronic device about an axis of the electronic device. The calculated angle is compared against a threshold angle and the captured facial image data is decided to be a replay when the calculated angle is greater than the threshold angle.

An aspect of the present disclosure provides a non-transitory computer-readable recording medium in an electronic device for enhancing liveness detection of facial image data. The non-transitory computer-readable recording medium stores instructions which when executed by a hardware processor perform the steps of the methods described above.

