Identity Masking via Motion Blur and Face Replacement
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Solution Overview
Problem
Existing automatic face detection techniques in images suffer from inaccuracies, including false positives and undetected face regions, and conventional identity masking methods alter the image appearance adversely.
Innovation Solution
A system that uses a face detector to identify possible face regions, with an identity masker applying motion blur or face replacement algorithms to obscure identities, and incorporates skin color analysis to verify and reject false positives, ensuring accurate and privacy-preserving image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional face detection techniques are used to identify face regions, then processing can be automated, but detection accuracy deteriorates due to false positives and missed detections
Solution Approach 1:
The face detection process is divided into multiple independent detection modules, each detecting faces using different algorithms or criteria. The results are then combined and verified, allowing the system to maintain automation while improving overall detection accuracy through multiple perspectives.
Solution Approach 2:
The system incorporates feedback mechanisms where detected face regions are verified against multiple criteria and can be adjusted based on verification results. This allows the automated detection to correct its own errors, reducing false positives while maintaining automation.
2Reliability
If face regions are replaced with solid color or mosaic shapes to obscure identities, then privacy protection is achieved, but image appearance deteriorates
Solution Approach 1:
Instead of applying uniform masking to all detected face regions, the system applies different masking strategies to different regions based on their characteristics. Some regions use solid color, others use mosaics, and the verification process ensures only appropriate regions are masked, preserving overall image appearance while maintaining privacy protection.
Solution Approach 2:
The system changes the masking parameters dynamically based on the detected face region characteristics and verification results. By adjusting masking intensity, type, and location based on verified face properties, the system obscures identities effectively while minimizing impact on image appearance.
3Productivity
If face detection sensitivity is increased to detect all possible face regions, then detection coverage improves, but false positives increase
Solution Approach 1:
The detection process is segmented into initial broad detection and subsequent verification stages. The broad detection captures all possible face regions (high coverage), while the verification stage filters out false positives by checking against multiple criteria, thus maintaining both productivity and reliability.
Solution Approach 2:
The system initially applies excessive detection sensitivity to ensure all possible faces are captured, then uses verification to remove false positives. This partial excessive action approach ensures no faces are missed while the verification step corrects the false positives, achieving both high coverage and high accuracy.
Data Source
AI summary
A method and system of identity masking to obscure identities corresponding to face regions in an image is disclosed. A face detector is applied to detect a set of possible face regions in the image. Then an identity masker is used to process the detected face regions by identity masking techniques in order to obscure identities corresponding to the regions. For example, a detected face region can be blurred as if it is in motion by a motion blur algorithm, such that the blurred region can not be recognized as the original identity. Or the detected face region can be replaced by a substitute facial image by a face replacement algorithm to obscure the corresponding identity.