Transcript
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Handbook of Fingerprint
Recognition
Chapters 1 & 2
Presentation by
Konda Jayashree
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History of fingerprints
Human fingerprints have been discovered on a large number ofarchaeological artifacts and historical items
In 1684, the English plant morphologist, Nehemiah Grew, published the firstscientific paper reporting his systematic study on the ridge, furrow, and pore
structure
In 1788, a detailed description of the anatomical formations of fingerprintswas made by Mayer.
In 1823, Purkinji proposed the first fingerprint classification, which classified
into nine categories
Sir Francis Galton introduced the minutae features for fingerprint matchingin late 19th century
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History of fingerprints
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Formation of fingerprints
Fingerprints are fully formed at about seven months of fetus
development
General characteristics of the fingerprint emerge as the skin on
the fingertip begins to differentiate.
flow of amniotic fluids around the fetus and its position in the
uterus change during the differentiation process
Thus the cells on the fingertip grow in a microenvironment that is
slightly different from hand to hand and finger to finger
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Fingerprint sensing
Based on the mode of acquisition, a fingerprint image is classified
as
Off line image
Live-scan image
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There are a number of live-scan sensing mechanisms thatcan detect the ridges and valleys present in the fingertip
Examples are
Optical FTIR
Capacitive
Pressure-based
Ultrasound
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Fingerprint Representation
Fingerprint representation should have following two
properties
Saliency
Suitability
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Fingerprint feature extraction
Fingerprint pattern, when analyzed at different
scales, exhibits different types of features
global level - delineates a ridge line flow pattern
local level minute details can be identified
Very fine level intra-ridge details can be detected
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Difficulty in fingerprint matching
Fingerprint matching is a difficult problem due to large variability in differentimpressions of the same finger Main factors responsible for intra-class variations are: displacement, rotation,
partial overlap, non-linear distortion, variable pressure, skin condition, noiseand feature extraction errors
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Fingerprint Matching
A three class categorization of fingerprint
matching approaches is: Correlation based matching
Minutae based matching
Ridge feature based matching
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Fingerprint classification and Indexing
To reduce the search time and computational complexity
technique used to assign a fingerprint to one of the several pre-specified types
Only a limited number of categories have been identified, and there
are many ambiguous fingerprints
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Synthetic fingerprints
Performance evaluation of fingerprint recognition
systems is very data dependent
To obtain tight confidence intervals at very lowerror rates, large databases of images are
required and its expensive
To solve this problem synthetic fingerprint
images are introduced
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Designing Fingerprint recognition systems
The major issues in designing the fingerprint
recognition system includes
Defining the system working mode
Choosing the hardware and software components Dealing with exceptions
Dealing with poor quality fingerprint images
Defining effective administration and optimization
policy
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Designing Fingerprint recognition systems
The system designer should take into account several
factors Proven technology
System interoperability and standards
Cost/performance trade off
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Securing fingerprint recognition systems
Maintaining the fingerprint recognition
system is critical and requires resolving
the frauds like Repudiation
Coercion
Circumvention
Contamination Denial of service attacks
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Applications
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Fingerprint Sensing
Acquisition of fingerprint Images was
performed by two techniques
Off-line sensing
Live-scan sensing
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The general structure of fingerprint
scanner is shown in figure
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The main parameters characterizing a fingerprint image are
Resolution
Area
Number of pixels
Dynamic RangeGeometric Accuracy
Image Quality
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Off-line fingerprint Acquisition
Although the first fingerprint scanners were introduced more than 30 years
ago, still ink-technique is used in some applications
Why & What are the advantages?
Because it has the possibility of producing
Rolled impressions
Latent impressions
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Rolled fingerprint Impressions
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Latent fingerprint images
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Live scan fingerprint sensing
The most important part of a fingerprint
scanner is the sensor.
All the existing scanners belong to one of
the 3 families
Optical sensors
Solid state sensorsUltrasound sensors
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Optical sensors
FTIR (Frustrated Total Internal Reflection)
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FTIR with sheet prism
In this, sheet prism is used instead of glass prism
Only Advantage is
Mechanical Assembly is reduced to some extent
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Optical Fibers
In Optical Fibers, a significant reduction of thepackagiing size can be achieved by substituting prismand lens with a fiber optic platen
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Electro Optical
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Solid state sensors
These are designed to overcome the size and cost problems
Silicon based sensors are used in this
Neither optical components nor external CCD/CMOS image sensors are
needed
Four main effects have been produced to convert the physical information
into electrical signals
Capacitive
Thermal
Electric field
Piezo Electric
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Capacitive
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Thermal sensors
Works based on temperature differentials
Sensors are made of pyro electric material
Temperature differential produces an image, but
this image soon disappears because the thermal equilibrium is quickly reached
and pixel temperature is stabilized
Solution is sweeping method
Advantages Not sensitive to ESD
Can accept thick protective coating
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Electric field
Sensor consists of drive ring
This generates a sinusoidal signal and amatrix of active antennas
To image a fingerprint, the analogueresponse of each element in the sensor
matrix is amplified, integrated and digitized
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Piezo- Electric
Pressure sensitive sensors
Produce an electrical signal when mechanical stress isapplied to them
Sensor surface is made up of a non-conducting dielectricmaterial
Ridges and valleys are present at different distances fromthe surface , they result in different amounts of current
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Ultrasound sensors
Principle is Echography
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Ultrasound sensors
Advantages of Ultrasound sensors
Good Quality images
Disadvantages
Scanner is large
Mechanical parts are quite expensive
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Touch Vs Sweep
Drawbacks of Touch method
Sensor can become dirty
Visible latent fingerprints remains on the
sensor
Rotation of the fingerprint may be a problem
Strict trade-off between the cost and the size
of the sensing area
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Sweeping Method
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Advantages of Sweeping Method
Equilibrium is continuously broken when
sweeping, as ridges and valleys touch the
pixels alternately, introducing a continuous
temperature change
Sensors always look clean
No latent fingerprints remain
No rotation
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Drawbacks
Novice user may encounter difficulties
Interface must be able to capture asufficient number of fingerprint slices
Reconstruction of the image from theslices is time consuming
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Image Reconstruction from the slices
Main stages are Slice quality computation
Slice pair registration
Relaxation Mosaicking
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Algorithm for fingerprint recognition from the slices
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Fingerprint scanners and their features
Interface
Frames per second
Automatic finger detection
Encryption Supported operating systems
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Sensing area Vs Accuracy
Recognizing fingerprints acquired through small areasensors is difficult
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An interesting alternative to deal with small
sensing areas is fingerprint Mosaicking
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Storing and Compressing fingerprint images
Each fingerprint impression produces an image
of 768 x 768 ( when digitized at 500 dpi)
In AFIS applications, this needs more amount of
memory space to store these images Neither lossless methods or JPEG compression
techniques are satisfactory
A new compression technique called Wavelet
Scalar Quantization (WSQ) is introduced to
compress the images
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WSQ
Based on Adaptive scalar quantization
Performs following steps
Fingerprint image is decomposed into a number of spatial frequencysub-bands using a Discrete wavelet transform
the resulting DWT coefficients are quantized into discrete values the quantized sub-bands are concatenated into several blocks and
compressed using an adaptive Huffman-run length encoding
A compressed image can be decoded into the original image by applyingsteps in reverse order
WSQ compress a fingerprint image by a factor of 10 to 25
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