Dynamic Mask Selection via Emotion Detection and Social Graph Mapping
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Solution Overview
Problem
Current social networking systems lack the ability to automatically select dynamic masks that correspond to user emotions and contextual factors, limiting user expression and engagement in image and video content.
Innovation Solution
A system that automatically selects dynamic masks from a database based on identified emotions, user interests, social data, and location, using emotion detection and mapping tables to apply graphical features that move and change shape in sync with the object, enhancing user expression by exaggerating emotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic masks are automatically selected based on emotions and contextual factors, then user expression and engagement are enhanced, but system complexity increases
Solution Approach 1:
The system segments the mask selection process into distinct functional modules: emotion detection module that analyzes facial expressions, contextual factor analysis module that processes user interests and location data, mapping table module that stores emotion-to-mask relationships, and mask application module that renders the selected mask. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The patent introduces mapping tables as intermediary data structures that store pre-defined relationships between emotions and corresponding masks. These mapping tables act as mediators between the emotion detection system and the mask rendering system, eliminating the need for complex real-time decision logic and simplifying the overall system architecture while enabling versatile mask selection.
2Measurement precision
If multiple factors (emotions, user interests, location) are combined for mask selection, then mask selection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user profile information, interests, and contextual preferences in structured databases and mapping tables before actual mask selection occurs. This allows the system to quickly retrieve and combine multiple factors during runtime without performing complex computations, thereby improving selection accuracy while minimizing processing time.
Solution Approach 2:
The patent employs parameter changes by adjusting the weight and influence of different factors (emotions, user interests, location data) based on predefined thresholds and confidence levels. The system dynamically changes parameters such as emotion confidence thresholds and factor prioritization to optimize the balance between selection accuracy and processing speed, allowing fast retrieval when confidence is high and more thorough analysis when needed.
3Adaptability or versatility
If graphical features move and change shape in sync with the object, then user engagement improves, but computational requirements increase
Solution Approach 1:
The system implements dynamics by making the graphical mask features movable and transformable in sync with the detected object's movements and expressions. The mask parameters (position, scale, rotation, shape) are dynamically adjusted based on real-time object detection data, allowing the mask to adapt to changing conditions while maintaining efficient computation through parameter-based transformations rather than complex rendering operations.
Data Source
AI summary
In one embodiment, a method includes identifying a first user in an input image, accessing social data of the first user in the input image, where social data comprises information from a social graph of an online social network, selecting, based on social data of the first user in the input image, a mask from a set of masks, where the mask specifies one or more mask effects, and for each of the input images, applying the mask to the input image. The set of masks may comprise masks previously selected by friends of the first user within the online social network. The selected mask may be selected from a lookup table that maps the social data to the selected mask.