Face Identity Vector Editing for Fast Image Anonymization
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Solution Overview
Problem
Existing image identity removal systems are computationally intensive and inefficient, requiring pixel-by-pixel manipulation to change identities, which consumes significant computing resources and time.
Innovation Solution
Generate a new identity vector using methods such as Variational Auto-Encoder (VAE), Gaussian Mixture Model (GMM), or K-nearest neighbors (KNN) to encode and transform face identities, allowing for efficient identity change in the latent space rather than pixel space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel manipulation is used to change face identities, then identity transformation can be achieved, but computational intensity and processing time increase significantly
Solution Approach 1:
The patent transforms the identity manipulation problem from pixel space to latent vector space. Instead of manipulating individual pixels directly, the system operates on compressed identity representations (vectors) that capture essential facial characteristics. This dimensional transformation reduces the complexity from millions of pixels to a manageable vector space, enabling efficient identity transformation while maintaining accuracy.
Solution Approach 2:
The patent extracts the identity information from the image into a separate identity vector, decoupling it from the rest of the image data. This extraction allows the identity to be manipulated independently through vector operations (addition, subtraction, interpolation) without affecting other image attributes. The separated identity vector can be efficiently processed and then recombined with the original image content.
2Reliability
If pixel-by-pixel manipulation is used to remove identities, then identity removal can be achieved, but computing resources are consumed excessively
Solution Approach 1:
The system moves the identity removal operation from the high-dimensional pixel space to a lower-dimensional latent vector space. By representing identities as compact vectors and performing removal operations in this compressed space, the computational resources required are dramatically reduced while maintaining the effectiveness of identity removal. The vector operations are inherently more efficient than pixel-level manipulations.
Solution Approach 2:
The patent changes the operational parameters from pixel values to identity vector representations. This parameter transformation enables the use of efficient vector arithmetic operations (such as linear interpolation between identity vectors) to achieve identity removal and transformation. The changed parameters allow for computationally lightweight operations that preserve image quality while reducing resource consumption.
Data Source
AI summary
Systems and methods are provided for detecting, at a face detection model of a server, a face of a person having a first identity in an image. A face recognition model of the server may detect an identity vector for the detected face of the person in the image. A new identity vector may be generated based on at least the detected identity vector. A revised image may be generated using the generated new identity vector and the image to be output for display, where the face of the person having the first identity in the image has a second identity in the revised image based on the generated new identity vector. The server may transmit the revised image.