Lecture
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Frequently found in Android smartphones.
Advantages:
Disadvantages:
the finger is placed on an area of the display
There are two kinds:
The display illuminates the finger, and a camera beneath the display captures the image.
Advantages:
Disadvantages:
Uses ultrasound and builds a three-dimensional map.
Advantages:
Disadvantages:
the scanner is made as a separate small USB device
This is a standalone module that connects to a computer, laptop, terminal or POS system over USB.
Where it is used
How it works
Such a USB device may contain any of several sensor types inside:
In other words, a USB scanner is not a separate physical sensing principle but a form factor and connection option.
Advantages
Disadvantages
Simple diagram
Finger ↓ [USB fingerprint scanner] ↓ USB ↓ PC / terminal / cash register ↓ software compares the fingerprint with the template
Comparison table
| Sensor type | How it works | Advantages | Disadvantages |
|---|---|---|---|
| Optical | Photographs the fingerprint | Cheap, simple | Easier to fool, sensitive to dirt |
| Capacitive | Measures the electrical capacitance of the skin | Fast, compact | Poor with wet/dry fingers |
| Ultrasonic | Builds a 3D map with ultrasound | More secure, works through the display | More expensive |
| Thermal | Reads a thermal image | Can detect living skin | Depends on temperature |
| Radio-frequency | Analyzes the skin's response to an RF signal | Better against spoofing | More complex and more expensive |
Normally the device stores not the fingerprint image itself but a biometric template.
Fingerprint → features → mathematical template → secure storage
In smartphones the template is often stored in a protected area of the processor, for example:
Secure Enclave Trusted Execution Environment Secure Element
When unlocking, the new scan is compared with the stored template.
Causes:
Matching algorithms are used to compare previously stored fingerprint templates against candidate fingerprints for authentication purposes. To do this, either the original image must be directly compared with the candidate image, or certain features must be compared.
Preprocessing improves image quality by filtering out and removing extraneous noise. A minutiae-based algorithm is effective only for 8-bit grayscale fingerprint images. One reason for this is that an 8-bit grayscale fingerprint image is the fundamental basis for converting the image into a 1-bit image with a value of 1 for ridges and a value of 0 for valleys. This process improves edge detection, so that the fingerprint is rendered with high contrast, with the ridges shown in black and the valleys in white. Two further steps are required to optimize the quality of the input image: minutiae extraction and removal of false minutiae. Minutiae extraction is carried out by applying a ridge thinning algorithm, which removes redundant ridge pixels. As a result, the thinned ridges of the fingerprint image are labeled with a unique identifier to make further operations easier. Once the minutiae have been extracted, false minutiae are removed. Insufficient ink and cross-connections between ridges can give rise to false details, which in turn cause inaccuracy in the fingerprint recognition process.
Pattern-based algorithms compare the basic fingerprint patterns (arch, whorl and loop) between a previously stored template and a candidate fingerprint. This requires that the images can be aligned in the same orientation. To do this, the algorithm finds a center point in the fingerprint image and centers on it. In a pattern-based algorithm, the template contains the type, size and orientation of the patterns within the aligned fingerprint image. The candidate fingerprint image is compared graphically with the template to determine the degree to which they match.
Часть 1 Fingerprint Sensors: Types, Design and Operating Principles
Часть 2 3. Under-display scanner - Fingerprint Sensors: Types, Design and Operating
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