First and second regions of interest are be identified within an initial video stream captured by a video capture device located within a physical space, in which the first region of interest is associated with a first conference participant within the physical space and the second region of interest is associated with a second conference participant within the physical space. A first frame rate may be determined for the first region of interest based on information associated with the first conference participant, and a second frame rate may be determined for the second region of interest based on information associated with the second conference participant. A capture of a first video stream associated with the first region of interest at the first frame rate and a second video stream associated with the second region of interest at the second frame rate may then be caused.

An electronic device, computer program product, and method enable selection and presentation of media content associated with a person identified in a monitored space. The electronic device includes a device interface that is communicatively coupled to a media output device that presents media content in the monitored area. A controller of the electronic device is communicatively coupled to sensor(s) and the media output device. The controller presents a default selection of media content on the media output device. The controller monitors the sensor(s) to detect a presence of a person within or approaching the monitored area. The controller identifies the person. The controller selects, from among a plurality of available media content, media content that is associated with the identified person. The controller presents the selected media content on the media output device.

Methods and systems are provided for providing directional assistance to guide a user to position a camera for centering a person's face within the camera's field of view. A neural network system is trained to determine the position of the user's face relative to the center of the field of view as captured by an input image. The neural network system is trained using training input images that are generated by cropping different regions of initial training images. Each initial image is used to create a plurality of different training input images, and directional assistance labels used to train the network may be assigned to each training input image based on how the image is cropped. Once trained, the neural network system determines a position of the user's face, and automatically provides a non-visual prompt indicating how to center the face within the field of view.

A computing device stores a captured image as a transient image template when there is a failure in an image-based authentication of the captured image and the user of the computing device has provided user credentials that authorize the user to access the computing device. The transient image template may be used to further authorize the user to access the computing device during an established time period.

A system is disclosed. The system may include a modular control unit configured to control power to at least one load device. The modular control unit may include a backplate with a recess that includes a set of backplate electrical contacts. The system may also include a contact element configured to receive the power. The system may also include at least one device control assembly configured to be removably coupled to the backplate. The device control assembly may include a set of device control assembly electrical contacts configured to electrically couple with the set of backplate electrical contacts of the backplate when the at least one device control assembly is coupled to the backplate and configured to electrically decouple from the set of backplate electrical contacts when the at least one device control assembly is decoupled from the backplate.

Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

An information processing apparatus (100) includes an acquisition unit (122) that acquires a first image from which person region feature information regarding a region including other than a face of a retrieval target person is extracted, a second image in which a collation result with the person region feature information indicates a match, and a facial region is detected, and result information indicating a collation result between face information stored in a storage unit and face information extracted from the facial region, and a display processing unit (130) that displays at least two of the first image, the second image, and the result information on an identical screen.

A method for determining a value of at least one geometrico-morphological parameter of a subject wearing an eyewear. The method including obtaining at least one image of a head of the subject wearing the eyewear, identifying simultaneously, on the at least one image obtained, a set of remarkable points of the image of the eyewear and a set of remarkable points of the image of the head of the subject, using an image processing algorithm determined based on a predetermined database comprising a plurality of reference images of heads wearing an eyewear, the image processing algorithm being based on machine learning, and determining at least one value of a geometrico-morphological parameter taking into account the sets of remarkable points of the image of the eyewear and head of the subject identified.

Systems, apparatuses, methods, and computer program products are disclosed for providing continuous session authentication and monitoring. An example method includes authenticating, at a first time, a session for a user of the client device based on an authentication image data structure and a plurality of first video frames captured before the first time. The example method further includes extracting sample data from a monitor region for each of a plurality of second video frames captured after the first time and generating motion data based on the extracted sample data. The example method further includes detecting, at a second time, a re-authentication trigger event based on the motion data. Subsequently, the example method includes re-authenticating the session based on the authentication image data structure and a plurality of third video frames captured after the second time.

In some aspects, a computing device receives a scan of a code displayed on an order post located near a restaurant, determines that the code is associated with the restaurant, and automatically opens a software application and navigates the software application to an ordering page associated with the restaurant. The computing device initiates receiving, via the software application, input associated with an order, sends the input to a machine learning based software agent executing on a server, receives a predicted response to the input, provides the predicted response as audio output and/or displays the predicted response on the touchscreen display device. After the order is complete, the computing device sends order data associated with the order to the restaurant. After receiving an indication from the restaurant that the order is ready, the computing device indicates that the order is ready to be picked up.