Fingerprint Identification Using Color Model Standard Deviation
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Solution Overview
Problem
Conventional fingerprint identification devices are unable to effectively distinguish between real and fake fingerprints, leading to potential security breaches as they can be deceived by fake fingers with silicon gel impressions.
Innovation Solution
A fingerprint identification method and device that analyzes pixel data in different color models, calculates standard deviations, and uses preset threshold values to determine whether an object image is from a true finger, preventing fake fingers from being misidentified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fingerprint identification devices are used, then basic fingerprint capture is achieved, but the ability to distinguish real from fake fingerprints deteriorates
Solution Approach 1:
The patent transforms the captured fingerprint image from RGB color space to HSV color space, extracting hue, saturation, and value parameters. By analyzing the standard deviation of these color parameters and comparing them against threshold values, the system can distinguish real fingerprints from fake ones. Real fingerprints exhibit specific color characteristic ranges that fake fingerprints made of silicon gel do not replicate, thereby resolving the contradiction between basic identification capability and fake finger resistance.
2Reliability
If simple fingerprint capture is performed, then device complexity is low, but identification reliability deteriorates
Solution Approach 1:
The patent replaces complex hardware-based detection mechanisms with software-based image processing algorithms. Instead of using multiple sensors or complex optical systems to detect fake fingers, the invention uses computational methods: converting RGB images to HSV color space, calculating standard deviations of color parameters, and comparing against predefined thresholds. This substitution achieves reliable fake finger detection while maintaining relatively simple device architecture.
3Measurement precision
If conventional identification methods are used, then processing speed is fast, but measurement precision deteriorates
Solution Approach 1:
The patent extracts only the essential color information from the fingerprint image by converting to HSV color space and focusing specifically on the hue and saturation parameters. By calculating the standard deviation of these extracted color parameters and comparing them against threshold values, the system achieves precise fake finger detection without requiring analysis of all image data. This selective extraction maintains processing speed while improving identification precision.
Data Source
AI summary
A fingerprint identification method is provided. The fingerprint identification method includes following steps: obtaining an object image and storing a plurality of pixel data of the object image in a first color model format, where the pixel data include a plurality of first pixel values; converting the pixel data into a second color model format and obtaining a plurality of second pixel values based on the converted pixel data and a first gain value; calculating a plurality of third pixel values based on the first pixel values and the second pixel values; calculating a first standard deviation based on the third pixel values; and determining whether the first standard deviation being greater than a first preset threshold value, if the first standard deviation being greater than the first preset threshold value, recognizing the object image as a fingerprint image of a true finger.