Privacy-Safe Palm Print Image Generation with Structured Line Patterns
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Solution Overview
Problem
The field of palm print recognition lacks a large public dataset for model training due to the strong privacy of palm print images, leading to poor identity recognition effects and authenticity of randomly generated samples.
Innovation Solution
A method for generating sample palm print images by creating major and minor palm print lines based on preset distribution patterns, labeling candidate images with distribution information, and using a pre-trained model to enhance the authenticity and robustness of the recognition process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large public dataset is collected for model training, then the model learning effect is improved, but the privacy protection requirement is violated
Solution Approach 1:
The patent generates synthetic palm print images that copy the essential features and patterns of real palm prints without using actual personal data. These synthetic images replicate the structural characteristics (major lines, minor lines, patterns) needed for training while being completely fictional, thus providing training data without violating privacy.
Solution Approach 2:
The patent creates disposable synthetic palm print images that can be used for training purposes and then discarded. These synthetic images serve as a temporary, non-sensitive training resource that replaces the need to store and share real palm print data, eliminating long-term privacy concerns while maintaining training effectiveness.
2Quantity of substance
If randomly generated palm print samples are used, then the dataset size is increased, but the authenticity of the samples deteriorates
Solution Approach 1:
The patent systematically varies multiple parameters including major line patterns, minor line distributions, line thicknesses, and spatial arrangements to generate diverse synthetic palm prints. By changing these parameters according to statistical distributions observed in real palm prints, the synthetic images achieve both quantity and authenticity.
Solution Approach 2:
The patent performs preliminary analysis of real palm print datasets to extract and store characteristic patterns, line distributions, and structural relationships before generating synthetic images. This preliminary action ensures that the synthetic palm prints are generated with authentic characteristics from the outset, rather than attempting to correct randomness after generation.
Data Source
AI summary
A sample palm print image generation method includes generating a major palm print line according to a preset major palm print line distribution pattern, determining a palm print distribution region in which the major palm print line is located, generating a minor palm print line in the palm print distribution region based on a preset minor palm print line distribution pattern, generating a candidate palm print image based at least on the major palm print line and the minor palm print line, labeling the candidate palm print image with a candidate image label that represents a palm print distribution of the candidate palm print image including at least one of a major palm print line distribution or a minor palm print line distribution, and inputting the candidate palm print image to a palm print generation model to obtain a sample palm print image.