Published Media Expression Detection for User-Controlled Alerts
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Solution Overview
Problem
Users are often unaware of undesirable content depicting them online, such as unflattering facial expressions or inappropriate actions, and current systems do not effectively alert them to such content.
Innovation Solution
A media guidance application identifies media assets depicting a user, retrieves a set of undesirable expressions indicated by the user, and compares these expressions with the user's media assets to generate notifications when matches are found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users upload media assets to social media platforms, then content sharing and connectivity are improved, but users may be exposed to undesirable depictions without their knowledge
Solution Approach 1:
The system implements feedback by continuously monitoring media assets and notifying users when undesirable depictions are detected. The notification system provides real-time feedback to users about content that matches their specified undesirable criteria, allowing them to take corrective action.
Solution Approach 2:
The system performs preliminary action by proactively scanning and analyzing media assets before users become aware of undesirable depictions. The automated detection system identifies problematic content in advance and alerts users, enabling them to manage their digital presence before harmful content spreads further.
2Loss of information
If current tagging systems are used to identify users in images, then user identification is improved, but users remain unaware of the context and nature of their depictions
Solution Approach 1:
The system introduces an intermediary component that acts as a bridge between simple tagging systems and comprehensive image analysis. This intermediary layer processes media assets, applies expression recognition algorithms, and translates complex analysis results into user-friendly notifications, thereby recovering information loss without requiring users to directly manage complex detection systems.
3Measurement precision
If automated expression recognition is implemented, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing detection efforts on specific facial expressions and depictions that users have identified as undesirable. Rather than analyzing every aspect of every image in exhaustive detail, the system targets specific recognition tasks based on user preferences, achieving sufficient accuracy while reducing processing time and computational overhead.
Data Source
AI summary
Systems and methods for warning a user that media assets associated with another user depict the user with an undesired expression are provided. A plurality of media assets associated with a first user and depicting a second user may be identified. A set of expressions of the second user that the second user has indicated as undesirable may be retrieved. The depictions of the second user in the plurality of media assets and the expressions that the second user has indicated are undesirable may be compared. If it is determined that one or more of the media assets depict the second user with an undesirable expression, a notification may be generated to the second user indicating that one or more media assets of the first user depict the second user with an expression that the second user has indicated as undesirable.