Abstract: When a software update is provided to a device that implements a facial recognition authentication process, a new authentication algorithm to operate the facial recognition authentication process may be included as part of software update. For a period of time, the new authentication algorithm may operate a “virtual” facial recognition authentication process alongside operation of the existing facial recognition authentication process using the existing (e.g., earlier version) authentication algorithm. The performance of the new authentication algorithm in providing facial recognition authentication (as assessed by the “virtual” process) may be compared to the performance of the existing authentication algorithm in providing facial recognition authentication during the period of time.

Abstract: An operation of a facial recognition authentication process may fail to authenticate a user even if the user is an authorized user of the device. In such cases, the facial recognition authentication process may automatically re-initiate to provide another attempt to authenticate the user using additional captured images. For the new attempt (e.g., the retry) to authenticate the user, one or more criteria for the images used in the facial recognition authentication process may be adjusted. For example, criteria for distance between the camera and the user's face and/or occlusion of the user's face in the images may be adjusted before the new attempt to authenticate the user. Adjustment of these criteria may increase the likelihood that the authorized user will be successfully authenticated in the new attempt.

Abstract: In an embodiment, a device may include an attention detection service. The attention detection service may monitor various peripheral devices in the device for indications that a user is paying attention to the device. Various clients may register for notification of attention detection and attention lost (attention no longer detected) events, or may poll the service for the events. If a user is not paying attention to the device, it may be possible to take various actions to permit reduced energy consumption.

Abstract: When a software update is provided to a device that implements a facial recognition authentication process, a new authentication algorithm to operate the facial recognition authentication process may be included as part of software update. For a period of time, the new authentication algorithm may operate a “virtual” facial recognition authentication process alongside operation of the existing facial recognition authentication process using the existing (e.g., earlier version) authentication algorithm. The performance of the new authentication algorithm in providing facial recognition authentication (as assessed by the “virtual” process) may be compared to the performance of the existing authentication algorithm in providing facial recognition authentication during the period of time.

Abstract: In an embodiment, a device may include an attention detection service. The attention detection service may monitor various peripheral devices in the device for indications that a user is paying attention to the device. Various clients may register for notification of attention detection and attention lost (attention no longer detected) events, or may poll the service for the events. If a user is not paying attention to the device, it may be possible to take various actions to permit reduced energy consumption.

Abstract: Techniques are disclosed relating to biometric authentication, e.g., facial recognition. In some embodiments, a device is configured to verify that image data from a camera unit exhibits a pseudo-random sequence of image capture modes and/or a probing pattern of illumination points (e.g., from lasers in a depth capture mode) before authenticating a user based on recognizing a face in the image data. In some embodiments, a secure circuit may control verification of the sequence and/or the probing pattern. In some embodiments, the secure circuit may verify frame numbers, signatures, and/or nonce values for captured image information. In some embodiments, a device may implement one or more lockout procedures in response to biometric authentication failures. The disclosed techniques may reduce or eliminate the effectiveness of spoofing and/or replay attacks, in some embodiments.

Abstract: An orientation of an application user interface (e.g., text and/or content) on a display of a device may be determined by an orientation of the device relative to gravity. In situations where the orientation of the device relative to gravity does not provide a confident or accurate orientation for the application user interface, orientation of the application user interface may be determined from face orientation data. Face orientation data may be obtained from a face detection process operating on images of the user captured during a facial recognition process or during an attention detection process.

Abstract: When a device is successfully unlock using a facial recognition authentication process, feature vectors generated from images obtained during the facial recognition authentication process may be stored as temporary templates on the device. After a period of time, one of the temporary templates may be selected to be used as a “virtual” template for the device. For example, a median temporary template in the temporary templates may be selected as the virtual template. The performance of the virtual template may then be assessed over time and compared to the performance of templates generated from an enrollment process to determine if and how the virtual template is implemented on the device.

Abstract: In an embodiment, a device may include an attention detection service. The attention detection service may monitor various peripheral devices in the device for indications that a user is paying attention to the device. Various clients may register for notification of attention detection and attention lost (attention no longer detected) events, or may poll the service for the events. If a user is not paying attention to the device, it may be possible to take various actions to permit reduced energy consumption.

Abstract: Techniques described herein provide efficient object detection and tracking in video images, such as may be used for real-time camera control in power-limited mobile image capture devices. The techniques include performing object detection on a first subset of frames of an input video, detecting an object and object location in a first detection frame of the first subset of frames, and tracking the detected object on a second subset of frames of the input video after the first detection frame.

Abstract: Techniques described herein provide efficient object detection and tracking in video images, such as may be used for real-time camera control in power-limited mobile image capture devices. The techniques include performing object detection on a first subset of frames of an input video, detecting an object and object location in a first detection frame of the first subset of frames, and tracking the detected object on a second subset of frames of the input video after the first detection frame, wherein the second subset does not include frames of the first subset.

Abstract: In an embodiment, a device may include an attention detection service. The attention detection service may monitor various peripheral devices in the device for indications that a user is paying attention to the device. Various clients may register for notification of attention detection and attention lost (attention no longer detected) events, or may poll the service for the events. If a user is not paying attention to the device, it may be possible to take various actions to permit reduced energy consumption.

Abstract: Detection of a user paying attention to a device may be used to enable or support biometric security (e.g., facial recognition) enabled features on the device. Images captured by a camera on the device may be used to determine if the user is paying attention to the device. Facial features of the user's face in the images may be assessed to determine if the user is paying attention to the device. Facial features may be assessed through comparison of feature vectors generated from the captured image feature to a set of known feature vectors. The known feature vectors for attention may be generated using a machine learning process.

Abstract: An electronic device is disclosed. The electronic device includes a first input device; a second input device capable of operating in a first operating mode; and a system management module in communication with the first input device and the second input device. The system management module is configured for switching the second input device to a second operating mode in response to detecting, by the first input device, a presence of a user without receiving any operation-specific input from the user.

Abstract: In an embodiment, a device may include an attention detection service. The attention detection service may monitor various peripheral devices in the device for indications that a user is paying attention to the device. Various clients may register for notification of attention detection and attention lost (attention no longer detected) events, or may poll the service for the events. If a user is not paying attention to the device, it may be possible to take various actions to permit reduced energy consumption.

Abstract: An orientation of an application user interface (e.g., text and/or content) on a display of a device may be determined by an orientation of the device relative to gravity. In situations where the orientation of the device relative to gravity does not provide a confident or accurate orientation for the application user interface, orientation of the application user interface may be determined from face orientation data. Face orientation data may be obtained from a face detection process operating on images of the user captured during a facial recognition process or during an attention detection process.

Abstract: Techniques described herein provide efficient object detection and tracking in video images, such as may be used for real-time camera control in power-limited mobile image capture devices. The techniques include performing object detection on a first subset of frames of an input video, detecting an object and object location in a first detection frame of the first subset of frames, and tracking the detected object on a second subset of frames of the input video after the first detection frame.

Abstract: Techniques described herein provide efficient object detection and tracking in video images, such as may be used for real-time camera control in power-limited mobile image capture devices. The techniques include performing object detection on a first subset of frames of an input video, detecting an object and object location in a first detection frame of the first subset of frames, and tracking the detected object on a second subset of frames of the input video after the first detection frame, wherein the second subset does not include frames of the first subset.

Abstract: An operation of a facial recognition authentication process may fail to authenticate a user even if the user is an authorized user of the device. In such cases, the facial recognition authentication process may automatically re-initiate to provide another attempt to authenticate the user using additional captured images. For the new attempt (e.g., the retry) to authenticate the user, one or more criteria for the images used in the facial recognition authentication process may be adjusted. For example, criteria for distance between the camera and the user's face and/or occlusion of the user's face in the images may be adjusted before the new attempt to authenticate the user. Adjustment of these criteria may increase the likelihood that the authorized user will be successfully authenticated in the new attempt.

Abstract: A facial recognition authentication on a device having a camera may operate with multiple enrollment profiles on the device. Multiple enrollment profiles may include separate profiles for different appearances of a user and/or separate profiles for different users authorized to use the device. The enrollment profiles may be generated using an enrollment process where the enrollment process is operated separately to generate each of the different enrollment profiles. During the facial recognition authentication process, a user may unlock the device by having a matching score for image(s) captured of the user that that exceeds an unlock threshold for at least one of the enrollment profiles. The user may have a matching score that exceeds the unlock threshold for multiple enrollment profiles. Each enrollment profile unlocks the device for the user may be updated using a template update process that operates independently for each enrollment profile.