Facial Expression Alterations for Teleconference Emotion Clarity
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Solution Overview
Problem
During teleconference sessions, it is difficult for users to identify the emotions of others through captured images, as facial expressions can be subtle and less apparent compared to physical interactions.
Innovation Solution
A computing device with a processor and memory resource, utilizing field-programmable gate array (FPGA) machine learning hardware, intercepts and analyzes image data from an imaging device to determine emotions and applies alterations to exaggerate facial expressions based on detected emotions, which are then displayed to other users, enhancing emotional identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If facial expressions are captured during teleconference sessions, then users can communicate remotely, but facial expressions become subtle and difficult to identify
Solution Approach 1:
The system changes the parameter of facial expression intensity by detecting subtle facial movements and transforming them into exaggerated expressions. The controller modifies the captured facial expression parameters (such as mouth curvature, eyebrow position, eye shape) to amplify the emotional signal, making emotions more visible while maintaining the original communication function
Solution Approach 2:
The controller acts as an intermediary between the camera capture and the display output. It processes the captured facial expressions through emotion detection algorithms and applies transformations to enhance emotional visibility, serving as a mediator that bridges the gap between subtle real-world expressions and the need for clear remote communication
2Ease of operation
If facial expressions are exaggerated to improve emotion recognition, then emotional identification becomes easier, but the natural appearance of the user is altered
Solution Approach 1:
The system applies partial exaggeration only to specific facial features that convey emotional information (such as mouth corners, eyebrows, eyes) rather than altering the entire face uniformly. The transformation is excessive only to the degree necessary for emotion recognition, applying amplification factors selectively to maintain natural appearance while improving emotion visibility
Solution Approach 2:
Different regions of the face are transformed with different exaggeration levels based on their emotional significance. High-emotion-information areas (mouth, eyebrows, eyes) receive greater transformation, while other facial regions maintain their natural appearance, creating a localized quality enhancement that improves emotion recognition without overly distorting the user's overall appearance
Data Source
AI summary
In some examples, the disclosure describes a device, comprising: a processor resource, and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: determine an emotion based on a facial expression of a user captured within an image, apply a plurality of alterations to the image to exaggerate the facial expression of the user when the emotion has continued for a threshold quantity of time, and remove the plurality of alterations to the image when the emotion of the user has changed.