Abstract: An example method includes receiving a first image and a second image of a face of a user, where one or both images have been granted a match by facial recognition. The method further includes detecting a liveness gesture based on at least one of a yaw angle of the second image relative to the first image and a pitch angle of the second image relative to the first image, where the yaw angle corresponds to a transition along a horizontal axis, and where the pitch angle corresponds to a transition along a vertical axis. The method further includes generating a liveness score based on a yaw angle magnitude and/or a pitch angle magnitude, comparing the liveness score to a threshold value, and determining, based on the comparison, whether to deny authentication to the user with respect to accessing one or more functionalities controlled by the computing device.

Abstract: An example method includes receiving a first image and a second image of a face of a user, where one or both images have been granted a match by facial recognition. The method further includes detecting a liveness gesture based on at least one of a yaw angle of the second image relative to the first image and a pitch angle of the second image relative to the first image, where the yaw angle corresponds to a transition along a horizontal axis, and where the pitch angle corresponds to a transition along a vertical axis. The method further includes generating a liveness score based on a yaw angle magnitude and/or a pitch angle magnitude, comparing the liveness score to a threshold value, and determining, based on the comparison, whether to deny authentication to the user with respect to accessing one or more functionalities controlled by the computing device.

Abstract: The present disclosure includes systems and methods for creating and sharing inline commentary relating to media within an online community, for example, a social network. The inline commentary can be one or more types of media, for example, text, audio, image, video, URL link, etc. In some implementations, the systems and methods either receive media that is live or pre-recorded, permit viewing by users and receive selective added commentary by users inline. The systems and methods are configured to send one or more notifications regarding the commentary. In some implementations, the systems and methods are configured to receive responses by other users to the initial commentary provided by a particular user.

Abstract: An example method includes receiving a first image and a second image of a face of a user, where one or both images have been granted a match by facial recognition. The method further includes detecting a liveness gesture based on at least one of a yaw angle of the second image relative to the first image and a pitch angle of the second image relative to the first image, where the yaw angle corresponds to a transition along a horizontal axis, and where the pitch angle corresponds to a transition along a vertical axis. The method further includes generating a liveness score based on a yaw angle magnitude and/or a pitch angle magnitude, comparing the liveness score to a threshold value, and determining, based on the comparison, whether to deny authentication to the user with respect to accessing one or more functionalities controlled by the computing device.

Abstract: An example method includes capturing, by a camera of a mobile computing device, an image, determining whether the image includes a representation of at least a portion of a face, and, when the image includes the representation of at least the portion of the face, analyzing characteristics of the image. The characteristics include at least one of a tonal distribution of the image that is associated with a darkness-based mapping of a plurality of pixels of the image, and a plurality of spatial frequencies of the image that are associated with a visual transition between adjacent pixels of the image. The method further includes classifying, by the mobile computing device, a quality of the image based at least in part on the analyzed characteristics of the image.

Abstract: A method can include receiving a first signal from a first sensor of a mobile computing device, where the first signal represents a first action of a user of the mobile computing device. In accordance with this example, the method further includes activating a second sensor of the mobile computing device based at least in part on the first signal. The method additionally can include receiving a second signal from the second sensor, where the second signal represents a second action of the user of the mobile computing device. The method further can include activating an input device of the mobile computing device based at least in part on the first and second signals. In some examples, the method may be implemented by one or more processors of a mobile computing device.

Abstract: An example method includes receiving a first image and a second image of a face of a user, where one or both images have been granted a match by facial recognition. The method further includes detecting a liveness gesture based on at least one of a yaw angle of the second image relative to the first image and a pitch angle of the second image relative to the first image, where the yaw angle corresponds to a transition along a horizontal axis, and where the pitch angle corresponds to a transition along a vertical axis. The method further includes generating a liveness score based on a yaw angle magnitude and/or a pitch angle magnitude, comparing the liveness score to a threshold value, and determining, based on the comparison, whether to deny authentication to the user with respect to accessing one or more functionalities controlled by the computing device.

Abstract: An example method includes capturing, by a camera of a computing device, an image including at least a face of a user, calculating a face template of the face of the user in the image, and analyzing the face template to determine whether the face includes at least one of a removable facial feature that decreases a level of distinctiveness between two faces and a non-removable facial feature that decreases a level of distinctiveness between two faces. When the face includes the removable facial feature, the method further includes outputting a notification for the user to remove the removable facial feature. When the face includes the non-removable facial feature, the method further includes adjusting a first similarity score threshold to a second similarity score threshold.

Abstract: An example method includes capturing, by a camera of a computing device, an image including at least a face of a user, calculating a face template of the face of the user in the image, and analyzing the face template to determine whether the face includes at least one of a removable facial feature that decreases a level of distinctiveness between two faces and a non-removable facial feature that decreases a level of distinctiveness between two faces. When the face includes the removable facial feature, the method further includes outputting a notification for the user to remove the removable facial feature. When the face includes the non-removable facial feature, the method further includes adjusting a first similarity score threshold to a second similarity score threshold.

Abstract: An example method includes receiving first and second images of a face of a user, where one or both images have been granted a match by facial recognition. The method includes identifying at least one facial landmark in the first image and at least one corresponding facial landmark in the second image, and extracting a first sub-image from the first image, where the first sub-image includes a representation of the at least one facial landmark. The method includes extracting a second sub-image from the second image, where the second sub-image includes a representation of the at least one corresponding facial landmark, detecting a facial gesture by determining whether a sufficient difference exists between the second sub-image and first sub-image to indicate the facial gesture, and determining, based on detecting the facial gesture, whether to deny authentication to the user with respect to accessing functionalities controlled by the computing device.

Abstract: An example method includes capturing, by a camera of a mobile computing device, an image, determining whether the image includes a representation of at least a portion of a face, and, when the image includes the representation of at least the portion of the face, analyzing characteristics of the image. The characteristics include at least one of a tonal distribution of the image that is associated with a darkness-based mapping of a plurality of pixels of the image, and a plurality of spatial frequencies of the image that are associated with a visual transition between adjacent pixels of the image. The method further includes classifying, by the mobile computing device, a quality of the image based at least in part on the analyzed characteristics of the image.

Abstract: Methods and apparatuses are disclosed for dynamic switching of user profiles on computing devices. In one method, the computing device identifies a first user profile under which the computing device is operating. The first user profile is associated with a first user value indicative of a first user. The computing device receives an image from an image-sensing device, generates a current user value indicative of a current user based on the received image, and determines if the current user value corresponds to the first user value. If the current user value does not correspond to the first user value, the computing device configures at least some programs operating on the computing device using a second user profile that is selected based on the current user value. If the current user value does correspond to the first user value, the computing device continues to operate using the first user profile.

Abstract: A method for dynamic switching of user profiles on a computing device. The computing device is coupled to at least one image-sensing device and can be configured using a plurality of stored user profiles. The method includes receiving at least one image from the at least one image-sensing device and generating a current user value based on the at least one received image. The method further includes determining if the current user value corresponds to at least one stored user value corresponding to a stored user profile. If the current user value corresponds to at least one stored user value, the method includes retrieving the stored user profile and configuring at least some programs operating on the computing device using the retrieved user profile. If the current user value does correspond to at least one stored user value, the method includes configuring the computing device using an alternative method.

Abstract: An example method includes initiating an emission, by a computing device, of at least one light beam, receiving, from an image capture device of the computing device, an image of a face of a user, and identifying at least one representation of an eye of the user in the image. The method further includes determining, by the computing device, whether at least one reflection of light from the light beam is associated with a cornea of the representation of the eye of the user in the image. The method further includes determining, based at least in part on the first determination, whether to deny authentication of the user with respect to accessing one or more functionalities controlled by the computing device, wherein the authentication is denied independent of performing facial recognition based at least in part on the image.

Abstract: In general, this disclosure describes techniques for assessing image quality of captured facial images. An example method includes capturing an image, generating a facial detection confidence score based in part on a likelihood that a representation of at least a portion of a face is included in the image, generating a facial landmark detection confidence score based at least in part on a likelihood that representations of facial landmarks are accurately identified in the image, and generating a geometric consistency score based at least in part on a difference between a point of intersection between a nose base and a line segment that passes through each eye and a midpoint of the line segment. The method also includes generating an image quality score based in part on a combination of the confidence scores, and the consistency score, and classifying an image quality based on the image quality score.