Biological Part Feature Extraction for Accurate Identity Recognition
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Solution Overview
Problem
Current identity recognition technologies based on biological features, such as hand shape, fingerprint, or palm print, suffer from low accuracy due to inadequate extraction of biological features from collected images.
Innovation Solution
An identity recognition method that involves obtaining a biological part image, determining the feature form type, extracting respective form features of pixels based on matching image feature forms, and performing feature matching to obtain an identity recognition result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional biological feature extraction methods are used, then the process is simple, but the accuracy of identity recognition is low
Solution Approach 1:
The patent segments the biological part image into multiple pixel-level feature extraction units, where each unit extracts features from specific coverage pixels. This segmentation approach allows for more detailed and accurate feature extraction compared to traditional holistic methods, directly improving identity recognition accuracy while maintaining manageable complexity through modular processing
Solution Approach 2:
The patent introduces a new dimension of feature extraction by combining distribution positions of coverage pixels to form image feature forms. This goes beyond traditional intensity-based extraction by incorporating spatial distribution information, thereby enhancing the discriminative power of extracted features and improving recognition accuracy
2Measurement precision
If image feature forms combining distribution positions of pixels are used, then feature extraction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the computational task into multiple independent feature extraction units, each processing a specific subset of pixels. This segmentation allows for parallel computation and optimized resource allocation, reducing the overall computational burden while maintaining high extraction accuracy through specialized processing in each unit
Solution Approach 2:
The patent applies different feature extraction strategies to different regions or types of pixels based on their local characteristics. By tailoring the extraction approach to local image features rather than applying a uniform method across the entire image, the system achieves high accuracy while minimizing unnecessary computational overhead in less critical regions
Data Source
AI summary
An identity recognition method, includes: obtaining a biological part image acquired for a target part of a to-be-recognized user; determining a feature form type of the target part, and extracting respective form features of pixels from the biological part image based on an image feature form matching the feature form type, where the image feature form is a form obtained after respective distribution positions of feature extraction coverage pixels are combined, and the feature extraction coverage pixels are pixels that are in the biological part image and targeted by each feature extraction; obtaining a biological part feature of the to-be-recognized user based on the respective form features of the pixels; and performing feature matching on the biological part feature and registered part features of registered users, to obtain feature matching results, and determining an identity recognition result of the to-be-recognized user based on the feature matching results.