State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.

According to various embodiments, the an electronic device includes: a communication module comprising communication circuitry, a camera, a display, and a processor, wherein the processor is configured to control the electronic device to: make a video call connection with at least one counterpart device through the communication module, analyze user emotion\ based on a user face recognized in an image acquired from the camera in response to an utterance of a speaker of the at least one counterpart device, and display a reaction induction object inducing a suitable reaction according to user emotion varying in response to the utterance of the speaker on the display.

A virtual visor system is disclosed that includes a visor having a plurality of independently operable pixels that are selectively operated with a variable opacity. A camera captures images of the face of a driver or other passenger and, based on the captured images, a controller operates the visor to automatically and selectively darken a limited portion thereof to block the sun or other illumination source from striking the eyes of the driver, while leaving the remainder of the visor transparent. The visor system advantageously detects certain combinations of facial expression and head gestures from which an error or issue with the operation of the visor system can be inferred. In response to such designated combinations of head gestures and facial expressions, the visor system adapts one or more operating or calibration parameters of the visor system to provide more accurate updates to the optical state of the visor.

A detection device includes a processing circuit that is configured to acquire an imaging video of a user to be authenticated, specify categories of micro-expressions of the user using the acquired imaging video, and determine whether an appearance pattern of each specified category for the micro-expressions is normal.

A system and method for tracking multifunction peripheral device user satisfaction captures user images and audio during device operation. User characteristics such as gestures, posture, spoke words or facial expressions are used in conjunction with device status information to determine whether the user is satisfied with the device. If not, remedial action is initiated, such as launching a virtual assistant on the multifunction peripheral touchscreen or summoning of a human assistant.

System, methods, and other embodiments described herein relate to an improved approach to providing gratitude between consumers and workers. In one embodiment, a method includes acquiring from a camera within a manufacturing facility, source video of manufacturing of different stages of a product. The method includes identifying, from the source video, segments associated with the product. The method includes generating a combined video from the segments. The method includes providing the combined video to a consumer associated with the product.

Aspects of the disclosure provide an image processing method, an image processing terminal, an image processing apparatus, and a non-transitory computer-readable storage medium. The method can include performing face detection on a target image to detect a face region in the target image. The method includes determining an expression class of the detected face region based on a trained expression recognition model and determining whether the determined expression class of the detected face region is a target expression class. If the determined expression class is the target expression class, head detection is performed to identify a head region in the target image and the identified head region is edited. If the determined expression class is not the target expression class, the head detection is not performed on the target image.

Methods, systems and media for context-aware estimation of student attention in online learning are described. An attention monitoring system filters or restricts the time periods in which student attention is monitored or assessed to those time periods in which student attention is important. These time periods of high attention importance may be determined by processing data from the teacher, such as audio data representing the teacher's voice and/or visual presentation data representing slides or other visual material being presented to the students. Various types of presenter data from the teacher and attendee data from the students may be used in assessing the importance of attention and each student's attention during each time period. The presenter may be provided with feedback in various forms showing student attention performance aggregated or segmented according to various criteria.

An action detection unit (110) analyzes monitor data obtained by monitoring a monitoring place, and detects a relevant action performed at the monitoring place and having a characteristic that is common to a violating action. A situation analysis unit (120) analyzes visual data obtained by photographing the monitoring place, and detects a surrounding situation of an action position equivalent to a position where the relevant action was performed. The situation analysis unit decides a nuisance degree of the relevant action on a basis of the surrounding situation. A presentation control unit (130) determines an alert level on a basis of the nuisance degree. The presentation control unit presents presentation information corresponding to the alert level with using a presentation apparatus installed at the monitoring place.

In an example, the present invention provides a meta data processing apparatus for processing sensor inputs and providing feedback to a user for an on-line course. The invention provides for storing the information on a public block chain using a plurality of servers.