Palm Vein Identification Using ROI Extraction and Binarization
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Solution Overview
Problem
Existing palm vein identification techniques face challenges with high computing time and unsatisfactory recognition effects due to high feature dimensionality and environmental dependencies, such as changes in palm gesture, size, or lighting conditions.
Innovation Solution
A method and device for palm vein identification that reduces computing time by extracting a region of interest (ROI) from a target palm vein image, acquiring feature data through filtering and binarization, and comparing it against registered data, using techniques like Gabor filters and affine transformation to enhance identification efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global feature extraction algorithms are used for palm vein identification, then recognition rate is improved, but computing time increases
Solution Approach 1:
The patent divides the palm vein image into multiple local regions (e.g., three zones: upper, middle, lower) and extracts features from each region separately. This segmentation approach reduces the dimensionality of global feature extraction while preserving important local vein characteristics, thereby maintaining recognition accuracy with reduced computing time.
Solution Approach 2:
The patent extracts only the essential local vein features from specific regions of interest rather than processing the entire palm vein image globally. By focusing on key vein segments that contain discriminative information, the method reduces computational burden while maintaining effective recognition performance.
2Loss of time
If curve matching techniques are used for palm vein identification, then computing time is reduced, but recognition effect becomes unsatisfactory when environmental conditions change
Solution Approach 1:
The patent applies different processing strategies to different local regions of the palm vein image. By extracting features from multiple localized areas rather than applying a single global matching approach, the system maintains robustness against environmental variations such as changes in palm gesture, size, or lighting conditions while keeping computational requirements low.
3Measurement precision
If high-dimensional feature data is used for palm vein identification, then recognition accuracy is improved, but data volume increases
Solution Approach 1:
The patent segments the feature extraction process into multiple independent local region analyses. Each region produces a compact feature vector, and the final feature representation is formed by combining these smaller vectors. This approach achieves high identification accuracy through comprehensive local feature capture while keeping the overall data volume manageable compared to global high-dimensional feature extraction.
Data Source
AI summary
The present teachings provides a palm vein identification device and a palm vein identification method. The method may comprise: acquiring a target palm vein image of a user; extracting a region of interest (ROI) from the target palm vein image of the user; acquiring feature data corresponding to the ROI, wherein the feature data are obtained by binarization processing; and comparing the feature data corresponding to the target palm vein image against feature data corresponding to a registered original palm vein image to perform identification on the target palm vein image of the user, wherein the feature data corresponding to the registered original palm vein image are obtained by calculation in advance.