In one aspect, a device may include at least one processor and storage accessible to the at least one processor. The storage may include instructions executable by the at least one processor to determine whether a first number of video conference attendees detected via input from one or more sensors is greater than a second number of logins to the video conference. The instructions may then be executable to present a graphical element on a display during the video conference responsive to determining that the first number is greater than the second number. The graphical element may indicate a first attendee that is detected via the one or more sensors but that is not associated with a login to the video conference.

An exemplary embodiment relates to the field of Automatic Face Recognition (AFR) systems. More specifically one exemplary embodiment relates at least to a method and a system capable of recognizing the face of a person using a device equipped with a camera of any kind and an associated computer, such as an embedded computer. The system is alternatively suitable to be implemented as an embedded system with minimal processing hardware capabilities, consuming very low power.

Systems and methods are described herein for displaying subjects of a portion of content. Media data of content is analyzed during playback, and a number of action signatures are identified. Each action signature is associated with a particular subject within the content. The action signature is stored, along with a timestamp corresponding to a playback position at which the action signature begins, in association with an identifier of the particular subject. Upon receiving a command, icons representing each of a number of action signatures at or near the current playback position are displayed. Upon receiving user selection of an icon corresponding to a particular signature, a portion of the content corresponding to the action signature is played back.

A person detection device configured to analyze a fisheye image obtained by a fisheye camera installed above a to-be-detected area to detect a person existing in the to-be-detected area includes a human body detector configured to detect a human body candidate from a fisheye image and output, as a detection result, a bounding box indicating a region on the fisheye image of each human body candidate detected, a reference storage configured to prestore a reference for a shape and/or size of the bounding box for each position or area on the fisheye image, and a false detection determining unit configured to compare the shape and/or size of the bounding box of the human body candidate contained in the detection result with the reference corresponding to a position where the human body candidate is detected to determine whether the human body candidate results from false detection.

The present disclosure provide a control method, a control apparatus, and an electronic device. The method includes processing the scene data to recognize a face region; processing the scene data to acquire depth information of the face region; determining a portrait region based on the face region and the depth information; and controlling the electronic device based on the portrait region.

A system and method for reading and interpreting a wide range of nonverbal communicative cues, to include facial expression, pose, gesture, posture, and voice intonation. The output of this system is a scale between zero and one—with the scale indicating the interpretation of the nonverbal communication and accompanying text describing the interpretation. The system determines how a person intends to react and determines whether the person's pronouncements are true or false.

Systems and methods for human-machine interaction. An adaptive behavioral control system of a human-machine interaction system controls an interaction sub-system to perform a plurality of actions for a first action type in accordance with a computer-behavioral policy, each action being a different alternative action for the action type. The adaptive behavioral control system detects a human reaction of an interaction participant to the performance of each action of the first action type from data received from a human reaction detection sub-system. The adaptive behavioral control system stores information indicating each detected human reaction in association with information identifying the associated action. In a case where stored information indicating detected human reactions for the first action type satisfy an update condition, the adaptive behavioral control system updates the computer-behavioral policy for the first action type.

Systems and methods are described herein for displaying subjects of a portion of content. Media data of content is analyzed during playback, and a number of action signatures are identified. Each action signature is associated with a particular subject within the content. The action signature is stored, along with a timestamp corresponding to a playback position at which the action signature begins, in association with an identifier of the particular subject. Upon receiving a command, icons representing each of a number of action signatures at or near the current playback position are displayed. Upon receiving user selection of an icon corresponding to a particular signature, a portion of the content corresponding to the action signature is played back.

Systems and methods are described for generating pixel image data, using a lensless camera, based on light that travels through a mask that with pattern masking the lensless camera. The system applies a transformation function to the pixel image data to generate frequency domain image data. The system inputs the frequency domain image data into a machine learning model, wherein the machine learning model does not have access to data that represents the pattern of the mask. The model is trained using a set of images with the feature that are captured by the flat, lensless camera through the mask. The system processes the frequency domain image data using the machine learning model to determine whether the pixel image data depicts the image feature. The system further performs an action based on determining that the pixel image data depicts the image feature.

A non-transitory computer readable medium storing an image processing program causing a computer to execute image processing, the image processing program causing the computer to execute: a person storage step of storing a person region that is a region that includes a person detected in a frame image and is smaller than the frame image; an image acquisition step of acquiring the frame image from a moving image; a detection region setting step of setting a detection region that is a region based on the person region stored in the person storage step in the frame image acquired in the image acquisition step; and a person detection step of detecting the person from the detection region set in the detection region setting step.