Abstract: A method for verifying the identity of a user is provided that includes generating, by a computing device, a parameter for each processed frame in a video of biometric data captured from a user. The parameter results from movement of the computing device during capture of the biometric data. Moreover, the method includes generating a signal for the parameter and calculating a confidence score based on the generated signal and a classification model specific to the user. The classification model is generated from other signals generated for the parameter. Furthermore, the method includes verifying the identity of the user as true when the confidence score is at least equal to a threshold score.

Abstract: A method for detecting user liveness is provided that includes selecting first and second images from a sequence of images. The first and second images are captured under different illumination conditions. The method further includes locating feature calculation windows in corresponding positions on the first and second images. Each window includes a first area and a second area. Moreover, the method includes calculating, by a computing device, a feature value for each window position based on pixels, within the windows located at the position, from the first and second images. Furthermore, the method includes calculating a feature vector from the feature values, calculating a confidence score from the feature vector, and determining the sequence of images includes images of a live user when the confidence score is equal to or greater than the threshold score.

Abstract: A method for displaying a visual aid is provided that includes calculating a distortion score based on an initial position of a computing device and comparing, by the computing device, the distortion score against a threshold distortion value. When the distortion score is less than or equal to the threshold distortion value, a visual aid is displayed having a first size and when the distortion score exceeds the threshold distortion value the visual aid is displayed at a second size.

Abstract: A method for displaying a visual aid is provided that includes calculating a distortion score based on an initial position of a computing device and comparing, by the computing device, the distortion score against a threshold distortion value. When the distortion score is less than or equal to the threshold distortion value, a visual aid is displayed having a first size and when the distortion score exceeds the threshold distortion value the visual aid is displayed at a second size.

Abstract: A method for capturing image data of a user is provided that includes displaying, by a computing device, a perspective view of a virtual computing device. Moreover, the method includes changing, by the computing device, the perspective view of the virtual computing device while the computing device moves, changing, by the computing device, the display of the virtual computing device to provide notification that the computing device is in an acceptable position for capturing image data of a user, and capturing, by a camera included in the computing device, facial image data of the user.

Abstract: A method for detecting user liveness is provided that includes selecting first and second images from a sequence of images. The first and second images are captured under different illumination conditions. The method further includes locating feature calculation windows in corresponding positions on the first and second images. Each window includes a first area and a second area. Moreover, the method includes calculating, by a computing device, a feature value for each window position based on pixels, within the windows located at the position, from the first and second images. Furthermore, the method includes calculating a feature vector from the feature values, calculating a confidence score from the feature vector, and determining the sequence of images includes images of a live user when the confidence score is equal to or greater than the threshold score.

Abstract: A method for detecting user liveness is provided that includes selecting first and second images from a sequence of images. The first and second images are captured under different illumination conditions. The method further includes locating feature calculation windows in corresponding positions on the first and second images. Each window includes a first area and a second area. Moreover, the method includes calculating, by a computing device, a feature value for each window position based on pixels, within the windows located at the position, from the first and second images. Furthermore, the method includes calculating a feature vector from the feature values, calculating a confidence score from the feature vector, and determining the sequence of images includes images of a live user when the confidence score is equal to or greater than the threshold score.

Abstract: A method for verifying the identity of a user is provided that includes generating, by a computing device, a parameter for each processed frame in a video of biometric data captured from a user. The parameter results from movement of the computing device during capture of the biometric data. Moreover, the method includes generating a signal for the parameter and calculating a confidence score based on the generated signal and a classification model specific to the user. The classification model is generated from other signals generated for the parameter. Furthermore, the method includes verifying the identity of the user as true when the confidence score is at least equal to a threshold score.

Abstract: A method for enhancing user liveness detection is provided that includes calculating, by a computing device, parameters for each frame in a video of captured face biometric data. Each parameter results from movement of at least one of the computing device and the biometric data during capture of the biometric data. The method also includes creating a signal for each parameter and calculating a similarity score. The similarity score indicates the similarity between the signals. Moreover, the method includes determining the user is live when the similarity score is at least equal to a threshold score.

Abstract: A method for capturing image data of a user is provided that includes displaying, by a computing device, a perspective view of a virtual computing device. Moreover, the method includes changing, by the computing device, the perspective view of the virtual computing device while the computing device moves, changing, by the computing device, the display of the virtual computing device to provide notification that the computing device is in an acceptable position for capturing image data of a user, and capturing, by a camera included in the computing device, facial image data of the user.

Abstract: A method for detecting user liveness is provided that includes selecting first and second images from a sequence of images. The first and second images are captured under different illumination conditions. The method further includes locating feature calculation windows in corresponding positions on the first and second images. Each window includes a first area and a second area. Moreover, the method includes calculating, by a computing device, a feature value for each window position based on pixels, within the windows located at the position, from the first and second images. Furthermore, the method includes calculating a feature vector from the feature values, calculating a confidence score from the feature vector, and determining the sequence of images includes images of a live user when the confidence score is equal to or greater than the threshold score.

Abstract: A method for enhancing user liveness detection is provided that includes calculating, by a computing device, a first angle and a second angle for each frame in a video of captured face biometric data. The first angle is between a plane defined by a front face of the terminal device and a vertical axis, and the second angle is between the plane defined by the front face of the terminal device and a plane defined by the face of the user. Moreover, the method includes creating a first signal from the first angles and a second signal from the second angles, calculating a similarity score between the first and second signals, and determining the user is live when the similarity score is at least equal to a threshold score.

Abstract: A method for enhancing user liveness detection is provided that includes calculating, by a computing device, parameters for each frame in a video of captured face biometric data. Each parameter results from movement of at least one of the computing device and the biometric data during capture of the biometric data. The method also includes creating a signal for each parameter and calculating a similarity score. The similarity score indicates the similarity between the signals. Moreover, the method includes determining the user is live when the similarity score is at least equal to a threshold score.

Abstract: A method of authenticating users is provided that includes storing data in a buffer. The data is within a temporal window and includes biometric data extracted from frames included in a video and quality feature values calculated for each frame. Each quality feature value corresponds to a different quality feature. Moreover, the method includes calculating a score for each different quality feature using the corresponding quality feature values, and determining a most recent frame included in the video includes biometric data usable in a biometric authentication matching transaction when the calculated score for each different quality feature satisfies a respective threshold score value.

Abstract: One embodiment of the invention provides an image processing method for use in fitting a deformable shape model to an acquired image. The deformable shape model specifies a set of target points whose motion is governed by the model. The method comprises, for each target point, generating a corresponding response image by: providing a feature detector to locate a given target point within the acquired image, wherein said feature detector includes a random forest comprising one or more decision trees; scanning a patch image across the acquired image to define multiple sampling locations with respect to the acquired image; for each sampling location of the patch image with respect to the acquired image, performing regression voting using the random forest to produce one or more votes for the location of the given target point within the acquired image; and accumulating the regression votes for all of the multiple sampling locations to generate said response image corresponding to the given target point.

Abstract: A method for enhancing user liveness detection is provided that includes calculating, by a computing device, parameters for each frame in a video of captured face biometric data. Each parameter results from movement of at least one of the computing device and the biometric data during capture of the biometric data. The method also includes creating a signal for each parameter and calculating a similarity score. The similarity score indicates the similarity between the signals. Moreover, the method includes determining the user is live when the similarity score is at least equal to a threshold score.

Abstract: A method for verifying the identity of a user is provided that includes generating, by a computing device, a parameter for each processed frame in a video of biometric data captured from a user. The parameter results from movement of the computing device during capture of the biometric data. Moreover, the method includes generating a signal for the parameter and calculating a confidence score based on the generated signal and a classification model specific to the user. The classification model is generated from other signals generated for the parameter. Furthermore, the method includes verifying the identity of the user as true when the confidence score is at least equal to a threshold score.

Abstract: A method for enhancing user liveness detection is provided that includes calculating, by a computing device, a first angle and a second angle for each frame in a video of captured face biometric data. The first angle is between a plane defined by a front face of the terminal device and a vertical axis, and the second angle is between the plane defined by the front face of the terminal device and a plane defined by the face of the user. Moreover, the method includes creating a first signal from the first angles and a second signal from the second angles, calculating a similarity score between the first and second signals, and determining the user is live when the similarity score is at least equal to a threshold score.

Abstract: A method for verifying the identity of a user is provided that includes generating, by a computing device, a parameter for each processed frame in a video of biometric data captured from a user. The parameter results from movement of the computing device during capture of the biometric data. Moreover, the method includes generating a signal for the parameter and calculating a confidence score based on the generated signal and a classification model specific to the user. The classification model is generated from other signals generated for the parameter. Furthermore, the method includes verifying the identity of the user as true when the confidence score is at least equal to a threshold score.

Abstract: A method for enhancing user liveness detection is provided that includes calculating, by a computing device, parameters for each frame in a video of captured face biometric data. Each parameter results from movement of at least one of the computing device and the biometric data during capture of the biometric data. The method also includes creating a signal for each parameter and calculating a similarity score. The similarity score indicates the similarity between the signals. Moreover, the method includes determining the user is live when the similarity score is at least equal to a threshold score.