Tenprint Card Fingerprint Segmentation With Frame Line Removal
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Solution Overview
Problem
Existing fingerprint matching systems face challenges in accurately segmenting individual fingerprint images from tenprint cards, particularly when fingerprints are not fully imprinted within frames or are protruding from them, leading to errors in imprinting and finger position specification, which degrade matching accuracy.
Innovation Solution
A tenprint card input device and method that automatically segments fingerprint images by using a data processing system to analyze and match rolled and slap print images, removing frame lines, and correcting fingerprint positions based on matching results, ensuring accurate segmentation and output of individual fingerprint images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic segmentation is implemented to improve processing efficiency, then productivity increases, but measurement precision of fingerprint positions deteriorates due to errors in imprinting and finger position specification
Solution Approach 1:
The system performs fingerprint matching between segmented fingerprint images and reference database images to verify segmentation accuracy. The matching results provide feedback to detect and correct position errors, ensuring that automatic segmentation maintains both high productivity and measurement precision through iterative verification and correction processes
Solution Approach 2:
The system removes frame lines and performs preliminary image processing before segmentation to eliminate sources of error. By pre-processing the tenprint card images to remove frame boundaries and standardize the input, the system prepares the data in advance for more accurate automatic segmentation and position detection
2Manufacturing precision
If frame lines are removed to improve segmentation accuracy, then manufacturing precision of fingerprint regions improves, but device complexity increases due to additional image processing steps
Solution Approach 1:
The system segments the tenprint card image into individual fingerprint regions by detecting and removing frame lines. This segmentation approach isolates each fingerprint image from its frame boundary, allowing independent processing of each fingerprint region and improving segmentation accuracy through systematic division of the complex image into manageable segments
Solution Approach 2:
The system introduces an intermediary frame line removal process between image input and fingerprint segmentation. This intermediary step acts as a mediator that eliminates frame boundary interference, creating a cleaner input for subsequent segmentation algorithms and improving overall manufacturing precision of fingerprint regions
3Measurement precision
If manual specification of finger positions is used to improve measurement precision, then fingerprint position accuracy improves, but productivity decreases due to time-consuming manual input
Solution Approach 1:
The system automatically detects and specifies finger positions through image processing and pattern recognition algorithms, eliminating the need for manual operator input. The fingerprint matching system self-identifies ridge patterns, minutiae points, and finger orientations, providing both high measurement precision and productivity through automated self-service processing
Data Source
AI summary
A fingerprint image processing device includes a memory, and a processor coupled to the memory. The processor performs operations. The operations include reading a tenprint card image which includes a plurality of fingerprint patterns and at least one ruled line to separate one fingerprint imprint area from another fingerprint imprint area, and extracting from the tenprint card image a fingerprint image which includes at least one of the fingerprint patterns, a part of a fingerprint imprint area, and a part of a next fingerprint imprint area.