What are Fingerprints?
The use of fingerprints within biometric applications is the oldest mode of computer-aided, personal identification and one of the most prevalent in use today.
The increasing use of fingerprints for the much larger market of personal authentication is due to factors including: small and inexpensive fingerprint capture devices, fast computing hardware, recognition rate and speed, to meet the needs of many applications, the explosive growth of network and Internet transactions, and the heightened awareness of the need for ease-of-use as an essential component of reliable security.
Verification vs Identification
Matching can be separated into verification or identification.
Verification
Is the comparison of a claimant fingerprint against an enrollee fingerprint, where the intention is that the claimant fingerprint matches the enrollee fingerprint
To prepare for verification, a person initially enrolls his or her fingerprint in to the verification system. A representation of that fingerprint into the verification system. A representation of
that fingerprint is stored in some compressed format along with the person’s name or other identity.
Subsequently, each access is authenticated by the person identifying him or herself, then applying the fingerprint to the system such that the identity can be verified
Also known as one-to-one matching.
Are you who you say you are?
Identification
Identification is the traditional domain of (criminal) fingerprint matching. A fingerprint of unknown ownership is matched against a database of known fingerprints, often to associate a crime with a known identity.
Also known as one-to-many matching
Who are you?
Feature Types: Microscopic
The more microscopic of the matching approaches is called the minutia matching.
Endings (where the ridge terminates) and bifurcation (where a ridge splits from a single path to two paths at a Y-junction) are two microscopic features.
Feature Types: Macroscopic
The more macroscopic matching approach to matching is called global pattern matching or pattern matching.
The different fingerprint patterns include:
- Arch
- Loop
- Whorl
Two other features used for matching are called the core and delta.
The core can be thought as the center of the fingerprint pattern and is where the ridges end.
The delta is a singular point from which three patterns deviate and is where the ridges begin and where they diverge.
Global aspects are important because they help with performance, cutting down the data size to make the verification process quicker.
Once core and delta is made out, it will rule out a load of other fingerprints.
Feature Types: Additional Information
There may be other features used for matching:
- Pores
- Size of the fingerprint
- Positions of scars and creases
Image Processing and Verification
Following image capture to obtain the fingerprint image, image processing is performed.
The objective for image processing is to achieve the best image possible.
The image processing steps are the following:
- Image noise reduction and enhancement
- Feature detection
- Matching
Image Specifications
Depending upon the fingerprint capture device, the image can have a range of specifications:
- Commonly, the pixels are 8-bit values and this yields and intensity range from 0 to 255.
- The dots per inch range from :
- 250, 625, 500
- Image are is from 0.5 inches^2 to 1.25 inches^2 with 1 inch being the standard
Image Enhancement
A fingerprint is one of the noisiest of image types
This is due to fingerprints being oily, dirty, wet etc.
Image enhancement stage is to reduce noise and to display the definition of ridges against valleys.
Feature Extraction
The fingerprint minutiae are found at the feature extraction stage.
There will always be extraneous minutiae found due to the noisy original image or due to artifacts introduced during matched filtering and thinning.
These extraneous features are reduced by using empirically determined thresholds.
The result of the feature extraction stage is what is called a minutia template. This is a list of minutiae with accompanying attribute values.
Anatomy of Fingerprints
Some fingerprint minutiae examples:
- Ending/Termination: A feature where a ridge terminates
- Bifurcation: Where a ridge splits from a single path to two paths at a y-junction
- Lake
- Point/Island
- Spur
- Crossover
- Also important to consider the direction of the fingerprint
Notes about Fingerprints
Crossover Rate: Describes a point where the False Rejection Rate (FRR) and False Acceptance Rate (FAR) are equal.
Describes the overall accuracy of a biometric system.
Scanners
Goal of the Scanner
The purpose of the scanner is to create a set of features of the fingerprint
Not necessarily an optical image
Three Main Types of Scanners
- Optical Fingerprint Scanner
- Capacitive Fingerprint Scanner
- Ultra Sonic Fingerprint Scanner
Type A: Optical Fingerprint Scanner
- Non-touch sensor
- Lens Based
- Think hardware systems physically
- 2D image
Type B: Capacitive Fingerprint Scanner
- Touch based
- Often used in phones
- 2D Image
- Uses a static charge (switches that turn on and off)
- Uses a pin like board which highlights the features of the fingerprint
- Sometimes called a Solid State Scanner
- Built in liveness detetion as it looks for electromagnetic charge of a human
Type C: Ultra Sonic Fingerprint Scanner
- Often used in phones from 2018 onward
- Touch Based
- 3D Image
- Ultrasound, same principle as medical devices
- Glass cover for protection
- No liveness detection, in theory the hardware capability of a type b scanner is more secure
Goals of Scanners
- Need to Acquire Image
- Feature Extraction
- Graph of Features
Notes about Scanners
Most fingerprint scanners have a green light for vein pattern detection and a red light for heat detection and themagrams.
UV light is used to read the fingerprint vein pattern for liveness detection.
Hence why more expensive readers are better because they have UV and red light detection.
Hardware is what makes fingerprint scanners secure.
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