Dynamic Facial Imaging for Residual Wrinkle Aging Risk Detection
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Solution Overview
Problem
Existing methods fail to accurately detect individuals at risk for premature skin aging through dynamic facial imaging, which is crucial for early intervention and prevention.
Innovation Solution
A computing system analyzes high-speed video data of facial expressions to quantify baseline and dissipating wrinkles, calculating residual wrinkles and mapping them to consumer risk for premature aging using age-specific thresholds and population correlation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static facial imaging is used to detect wrinkles, then the device complexity is low, but the measurement precision of premature aging detection is insufficient
Solution Approach 1:
The patent transitions from static facial imaging to dynamic facial imaging by capturing multiple video frames during facial expressions. The system analyzes wrinkle formation and dissipation across temporal sequences, enabling detection of residual wrinkles that indicate premature aging risk. This dynamic approach captures the transient nature of wrinkles during muscle movement and relaxation, significantly improving detection precision.
Solution Approach 2:
The patent introduces an intermediary processing system that captures video data, extracts facial regions, detects wrinkle characteristics across multiple frames, and generates risk assessments. This intermediary computational layer bridges the simple camera input and the complex diagnostic output, enabling precise premature aging detection through algorithmic analysis of dynamic wrinkle behavior.
2Measurement precision
If dynamic facial imaging with multiple frames is used, then the measurement precision improves, but the loss of time for data processing increases
Solution Approach 1:
The patent extracts only the essential information needed for wrinkle detection from the video data. Instead of processing all video frames equally, the system identifies and extracts key frames showing facial expression peaks and relaxation phases, then focuses computational resources on analyzing wrinkle characteristics in these critical moments. This extraction approach maintains high measurement precision while reducing overall processing time.
Solution Approach 2:
The patent performs preliminary actions by pre-identifying key facial landmarks and wrinkle regions of interest before full video processing. The system prepares detection algorithms and parameter settings in advance, so when video data arrives, the processing can proceed efficiently with pre-configured analysis pipelines, reducing the time loss associated with dynamic imaging.
3Reliability
If residual wrinkles are quantified through dynamic imaging, then the reliability of premature aging risk assessment improves, but the device complexity increases
Solution Approach 1:
The patent segments the complex task of aging assessment into distinct components: baseline wrinkle detection, dissipating wrinkle detection, residual wrinkle calculation, and risk level classification. Each segment is handled by specialized algorithms that process specific aspects of the dynamic imaging data. This segmentation improves reliability by ensuring thorough analysis of each parameter while managing device complexity through modular computational architecture.
Solution Approach 2:
The patent utilizes parameter changes in wrinkle characteristics over time as the core measurement mechanism. By tracking how wrinkle depth, area, and persistence change during facial expression and relaxation cycles, the system derives residual wrinkle parameters that reliably indicate premature aging risk. This parameter-based approach transforms complex visual data into quantifiable metrics that improve assessment reliability without requiring overly complex device architecture.
Data Source
AI summary
Systems, methods, and instrumentalities are described herein for identifying a user (e.g., a consumer) at risk for premature aging through dynamic facial imaging assessment. For example, a computing system may receive video data. The video data may be, or may include, one or more frames associated with the face of the user. The computing device may identify a frame from the video data. The computing device may determine a number of baseline wrinkles associated with the first state. The computing device may determine a number of dissipating wrinkles associated with the second state (e.g., a duration after the peak and relaxation of facial expression). Based on the number of baseline wrinkles and the number of dissipating wrinkles, the computing device may calculate a number of residual wrinkles between the first state and the second state. The computing device may map the number of residual wrinkles to the consumer risk for premature aging.