Transcript Slides

By:
Kirti Chawla
Definition
Biometrics utilize ”something you are” to authenticate identification. This might
include fingerprints, retina pattern, iris, hand geometry, vein patterns, voice
password, or signature dynamics.
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Building Blocks
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Sensor
Feature Extraction Algorithm
Search & Match Algorithm
Identity Database
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Building Blocks:
Sensor
A sensor that responds to biological stimulus, such as fingerprints, voice, retinas,
Thumb pressure dynamics to generate signal that can be measured or interpreted.
A typical sensor is solid-state device, which includes method to extract features, for
which it is targeted.
Fujitsu MBF 200 Solid State Fingerprint Sensor
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Building Blocks:
Feature Extraction Algorithm
A feature extraction algorithm detects and isolates portions of digital signal emanated out
of a sensor. The signal generated by sensor contains identifying properties, which needs
to be isolated and characterized. The algorithm creates descriptors or characteristic
attributes on per signal basis. An identifying feature is called minutiae and is stored in a
file called template. A template typically contains ~ 200 minutiae.
Typical features in a fingerprint
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Building Blocks:
Search & Match Algorithm
A search & match algorithm takes an input characteristic feature and compares it with
stored feature(s) and outputs either success or failure of the outcome. The features can
be stored to facilitate optimal search strategies using deterministic bounds in range
~O(Log2(N)xLog2(M)) to ~O(NxM), where N is no. of features, M is no. of templates.
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Minutiae belonging to a particular user
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Typical storage patterns of minutiae
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Building Blocks:
Identity Database
An Identity database is collection of templates on which a given search & match
algorithm operates to find whether a given input characteristic matches or not. There is a
slight variation in the algorithms that operate upon the identity database. The design of
Identi t y da tab as e affec ts t he al gori th ms ope rati ng on i t. A f eatu re (F ),
when operating on a Identity database (D) can have an evaluation criteria of matching
with All or majority (M). Symbolically, it can be represented as:
TRUE
When feature (F) matches with majority (M) of features in
Identity database (D)
Search_and_Match(F, A|M, D)
FALSE
When feature (F) does not matches with majority (M) of
features in Identity database (D)
Matching criterion of a feature in Identity database
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Physical Phenomenon(s)
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Non-Behavioral Phenomenon(s)
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Behavioral Phenomenon(s)
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Fingerprints
Retina Scans
Iris Scans
Voice Patterns
Blood vessel measurements
Keying rate
Breathing rate
Mouse movements
Utterance Patterns
Signature dynamics
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Working Setup
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Schematic(s)
Pseudo Code
Flow chart(s)
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Working Setup:
Schematic(s)
Sensor with Feature Extraction Algorithm
Identity Database
Learning or Enrollment Operation
Sensor with Feature Extraction Algorithm
Search & Match Algorithm
Identity Database
Verification Operation
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Working Setup:
Pseudo Code
Sensor Subsystem
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Search & Match Algorithm
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Identity Database
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Working Setup:
Flow chart(s)
Start
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Is there an
Interrupt ?
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Capture user data
Extract features
Make template
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No
If operation
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ENROLL
Send data to
Identity Database
Search & Match
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Stop
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Applications
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Security systems at pertinent places (Airports, Banks, …)
Alternative of SWIPE cards in Personnel Management
Animal breeding management
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Q & A