Palm Vein Identification Using Grouped Database Segmentation
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Solution Overview
Problem
Conventional palm vein identification systems face challenges in improving accuracy, reducing processing time, and enhancing overall efficiency.
Innovation Solution
A palm vein identification system that utilizes a rating method with a plurality of groupings to narrow down potential matches by comparing scans to predefined groups based on various factors, including location, hand geometry, and machine learning algorithms, thereby creating a match score to efficiently authenticate users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional palm vein identification systems compare scans directly to all database records, then comprehensive matching is achieved, but processing time increases and efficiency decreases
Solution Approach 1:
The database is segmented into multiple groups based on predefined factors such as location, time, and hand geometry characteristics. Instead of comparing a scan against all records in a single large database, the system divides the search space into smaller manageable groups, reducing the number of comparisons needed while maintaining comprehensive coverage of potential matches
Solution Approach 2:
The system performs preliminary actions by pre-categorizing database records into groups based on static attributes like location and hand geometry before the actual authentication process. This preliminary organization allows the system to quickly eliminate irrelevant groups and focus comparison efforts only on relevant candidate groups, significantly reducing processing time
2Adaptability or versatility
If the database size increases to improve identification coverage, then more candidates can be authenticated, but processing complexity and time increase
Solution Approach 1:
The system segments the growing database into multiple organized groups using predefined factors. This segmentation strategy allows the database to scale in size while maintaining manageable processing complexity, as each group can be independently processed and searched
Solution Approach 2:
Different groups in the database are organized with different characteristics (location-based groups, time-based groups, geometry-based groups). The system applies appropriate comparison strategies to each group type, optimizing processing for each local characteristic rather than using a uniform approach across the entire database
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy and efficiency of palm vein identification by narrowing down potential matches, allowing for faster and more reliable authentication processes.
Implementation Method 1
Typical palm scanners illuminates and scan the person's palm using near-infrared light, which is absorbed by deoxygenated blood flowing through the person's veins. The light is reflected back, causing the veins to appear black
Data Source
AI summary
A palm vein identification system includes a palm vein scanning device to capture a scan of a vein pattern of a candidate; the database having a groups, each group having a records, each records associated with a candidate record; the server having a processing unit to receive the scan from the communication system; create an image file associated with the scan; determine which of the groups the scan fits into; and narrow down a match associated with the scan based on which of the groups the scan fits into; the server authenticates the candidate based on the match.