Abstract: The present disclosure is directed to computer-implemented systems and methods for performing recognition over a network of devices. In general, the systems and methods implement a machine-learned recognizability model that can process information such as a person's voice, facial characteristics, or similar information to determine a recognizability score without necessarily generating or storing biometric information that could be used to identify the person. The recognizability score can act as a proxy for the quality of the information as a reference for biometric recognition that can be performed on other devices in the network of devices. Thus a single device can be used to enroll a person in the network (e.g., by capturing a number of photographs of the person). Thereafter, connection to the other devices can utilize a sensor (e.g., a camera) on the other devices to compare features of the reference information to the input received by the sensor.

Abstract: A transformed video and a source video may be synchronized according to implementations disclosed herein to provide tag information to the device receiving the transformed version of the video. A synchronization signal may be computed on the source video and the transformed video using a statistic such as mean pixel intensity. The synchronization signals computed for the transformed video and source video may be compared to determine a transformed video reference point location for the requested tag information. The requested tag information may be provided to the device receiving the transformed version of the video.

Abstract: Facial recognition can be used to determine whether or not a user has access privileges to a device such as a smartphone. It may be possible to spoof an authenticated user's image using a picture of the user. Implementations disclosed herein utilize time of flight and/or reflectivity measurements of an authenticated user to compare to such values obtained for an image of an object. If the time of flight distance and/or reflectivity distance for the object match a measured distance, then the person (i.e., object) may be granted access to the 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 an image capture device of a computing device, an image of a face of a user. The method further includes detecting, by the computing device, whether a distance between the computing device and an object represented by at least a portion of the image is less than a threshold distance, and, when the detected distance is less than a threshold distance, denying authentication to the user with respect to accessing one or more functionalities controlled by the computing device, where the authentication is denied independent of performing facial recognition based at least in part on the captured image.

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: Facial recognition can be used to determine whether or not a user has access privileges to a device such as a smartphone. It may be possible to spoof an authenticated user's image using a picture of the user. Implementations disclosed herein utilize time of flight and/or reflectivity measurements of an authenticated user to compare to such values obtained for an image of an object. If the time of flight distance and/or reflectivity distance for the object match a measured distance, then the person (i.e., object) may be granted access to the 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 transformed video and a source video may be synchronized according to implementations disclosed herein to provide tag information to the device receiving the transformed version of the video. A synchronization signal may be computed on the source video and the transformed video using a statistic such as mean pixel intensity. The synchronization signals computed for the transformed video and source video may be compared to determine a transformed video reference point location for the requested tag information. The requested tag information may be provided to the device receiving the transformed version of the video.

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: An example method includes capturing, by an image capture device of a computing device, an image of a face of a user. The method further includes detecting, by the computing device, whether a distance between the computing device and an object represented by at least a portion of the image is less than a threshold distance, and, when the detected distance is less than a threshold distance, denying authentication to the user with respect to accessing one or more functionalities controlled by the computing device, where the authentication is denied independent of performing facial recognition based at least in part on the captured image.

Abstract: In general, aspects of the present disclosure are directed to techniques for adjusting the threshold similarity score to match the facial features of an authorized user in a facial recognition system. Instead of a uniform threshold similarity score, representations of authorized faces enrolled in the computing device may be assigned a custom threshold similarity score based at least in part on the distinctiveness of the facial features in the representations of the authorized faces. A computing device can determine a distinctiveness of facial features of an enrolling user, and can determine a threshold similarity score associated with the facial features of the enrolling user based at least in part on the distinctiveness of the facial features of the enrolling 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 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.