Facial Feature Adjustment via Stability Detection
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Solution Overview
Problem
Current image data processing methods for facial beautification applications require manual adjustments, which can be cumbersome and inefficient, especially during live video or selfie capturing, as they only provide an overall fine-adjusted processing effect without allowing for precise adjustments of facial features.
Innovation Solution
An image data processing method that collects and extracts user images regularly, records position information of specific facial features, determines stability based on movement analysis, and performs image processing when a preset movement threshold is reached, allowing for automatic adjustments such as zoom-in or lip enhancement on the display interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual control is used for fine adjusting facial organs, then precise adjustment of specific facial features is achieved, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically detects facial features and applies fine adjustments without requiring manual user input. The terminal device performs self-service by monitoring facial organ positions and autonomously executing adjustment operations based on detected movement patterns, eliminating the need for users to manually control each facial feature adjustment.
Solution Approach 2:
The system changes the parameter of adjustment precision dynamically by switching between coarse and fine adjustment modes. When facial organs remain stable for a predetermined time, the system transitions to fine adjustment mode with higher precision parameters, automatically optimizing the adjustment precision based on detected stability conditions rather than requiring manual selection.
2Productivity
If overall fine adjusted processing is applied to image data, then processing speed is maintained, but precise adjustment of specific facial features is lost
Solution Approach 1:
The system segments the facial region into multiple independent facial organs (eyes, eyebrows, nose, mouth, etc.) and applies fine adjustment operations selectively to specific segments rather than processing the entire face uniformly. This allows precise adjustment of individual facial features while maintaining overall processing efficiency through targeted rather than comprehensive processing.
Solution Approach 2:
The system dynamically adjusts the processing granularity based on detected facial organ stability. When stability is detected, the system transitions from overall processing to targeted fine adjustment of specific facial organs, making the processing precision dynamic rather than static, thus balancing efficiency and precision adaptively.
3Measurement precision
If continuous monitoring of facial features is performed, then automatic adjustment accuracy is improved, but energy consumption and processing load increase
Solution Approach 1:
Instead of continuous monitoring, the system performs periodic detection at predetermined time intervals. The facial organ position is detected at discrete time points, and stability is determined by comparing positions across these periodic measurements, reducing energy consumption while maintaining sufficient detection accuracy for automatic adjustment triggers.
Solution Approach 2:
The system performs partial monitoring by focusing detection resources only on key facial organs that require adjustment rather than monitoring all facial features continuously. This partial action approach reduces processing load and energy consumption while maintaining sufficient accuracy for the specific adjustment needs.
Data Source
AI summary
Embodiments of the present disclosure provide an image data processing method, an image data processing apparatus, an electronic device and a storage medium. The image data processing method includes: during a preset first collecting time period, collecting regularly a plurality of first user images corresponding to a target user, and extracting first part image data in each of the plurality of first user images; recording position information of the first part image data in each of the plurality of first user images on a display interface; if it is determined that the target user is at a preset stable state, performing a statistical processing to obtain a total movement times corresponding to the first part image data within a preset statistical time period; and if the total movement times reaches a preset threshold, performing an image processing on second part image data on a current display interface.