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Biometric security Presentation Course Basic visit : http://www.dailygk.com/
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BIOMETRIC SECURITY SYSTEMS
A FUTURE WITHOUT
PASSWORDSEnd of “PASSWORD OVERLOAD”
WORKPLACEDESKTOP COMPUTERCORPORATE COMPUTER NETWORK INTERNET & E-MAIL
CLASSIFICATION
IDENTIFICATION
PHYSIOLOGICAL BEHAVIORAL
VERIFICATIONOR
OR
BIOMETRIC PROCESS
PresentPresent
BiometricBiometricCaptureCapture ProcessProcess
PresentPresent
BiometricBiometricCaptureCapture ProcessProcess
COMPARE
STORE
ENROLLMENT
VERIFICATION
NO MATCH
MATCH
TECHNOLOGIES
BIOMETRICS SECURITYFINGER
SIGNATURE
FACE VOICE
IRIS
HAND
IRIS RECOGNITION
Iris patterns are extremely complex.
Patterns are individual Patterns are formed by
six months after birth, stable after a year. They remain the same for life.
Imitation is almost impossible.
Patterns are easy to capture and encode
RETINA SCANNERS (continued)
•Main retina Main retina featuresfeatures
•Actual photo of Actual photo of retinaretina
IRIS SCANNERS• High resolution cameras capture image from up to 3 feet
away (usually 10 to 12 inches)
• Converts picture of the distinctive fibers, furrows, flecks, crypts, rifts, pits and coronas of the iris into a bar-code like identifier
• Template around 256 Bytes in size
• Human iris is distinct with 250 differentiating features
• The recognition of irises by their IrisCodes is based upon the failure of a test of statistical independence.
– Any given IrisCode is statistically guaranteed to pass a test of independence against any IrisCode computed from a different eye; but it will uniquely fail this same test against the eye from which it was computed.
FINGER RECOGNITION
Print showing various Print showing various types of Minutiaetypes of Minutiae
HOW IT WORKSSUBMISSION FEATURE EXTRACTION
IMAGE ENHANCEMENT MATCHING
FINGER PRINT (continued)• Fingerprint matching techniques can be placed into two categories: minutiae-
based and correlation based.
– Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach.
• It is difficult to extract the minutiae points accurately when the fingerprint is of low quality.
• Also this method does not take into account the global pattern of ridges and furrows.
– The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings.
• Correlation-based techniques (i.e. pattern matching) require the precise location of a registration point and are affected by image translation and rotation.
• Larger templates (often 2 – 3 times larger than minutiae-based)
FACE RECOGNITION
•Typical EigenfacesTypical Eigenfaces
•Utilizes two dimensional,
•global grayscale images
•representing distinctive
•characteristics of
•a facial image
•Variations of eigenface are
•frequently used as the basis of other face recognition
methods.
FACIAL (continued)• Eigenface: "one's own face," a technology patented at MIT that uses 2D
global grayscale images representing distinctive characteristics of a facial image. Most faces can be reconstructed by combining features of 100-125 eigenfaces. During enrollment, the user's eigenface is mapped to a series of numbers (coefficients). Upon a 1:1 match, a "live" template is matched against the enrolled template to obtain a coefficient variation. This variation either accepts or rejects the user.
• Local Feature Analysis (LFA): also a 2D technology, though more capable of accommodating changes in appearance or facial aspect (e.g., smiling, frowning). LFA uses dozens of features from different regions of the face; incorporates the location of these features. Relative distances and angles of the "building blocks" of the face are measured. LFA can accommodate 25-degree angles in the horizontal plane and 15 degrees in the vertical plane. LFA is a derivative of the eigenface method and was developed by Visionics, Corp.
FACE RECOGNITION
FACIAL (continued)• Varying light (i.e. outdoors) can affect accuracy• Some systems can compensate for minor changes such as
puffiness and water retention• Smiling, frowning, etc can affect accuracy• Some systems can be confused by glasses, beards, etc• Human faces vary dramatically over long term (aging) and
short term (facial hair growth, different hair styles, plastic surgery)
• Expected high rate of acceptance as people are already used to being photographed or monitored
• Best method for identification systems (e.g. airports)
VOICE RECOGNITION
• The software remembers the way you say each word.
• Voice recognition possible even though everyone speaks with varying accents and inflection.
• Telephony : the primary growth area
VOICE VERIFICATION
•It is these well-formed, regular patterns that are
unique to every individual. These patterns are created from the size and shape of the physical structure of a person's vocal tract. Since
no two vocal tracts are exactly the same, no two signal patterns can be the
same.
•A complete signal has an overall pattern, as well as
a much finer structure, called the frame. This
frame is the essence of voice verification
technology.
VOICE VERIFICATION•These unique
features consist of cadence, pitch, tone,
harmonics, and shape of vocal tract.
•The image at right shows how
characteristics of voice actually involve
much more of the body than just the
mouth.
Nasal Cavity
Nostral
Lip
Teeth
Tongue
Oral (or Buccal) Cavity
Jaw
Trachea
Lung
Diaphram
Hard palate
Soft palate
Phayngeal cavity
Larynx
Esophagus
HAND GEOMETRY
• 32,000-pixel CCD digital camera .
• The hand-scan device can process the 3-D images in less than 5 seconds & the verification usually takes less than 1 second.
• U.S INPASS PROGRAM
HAND/FINGER GEOMETRY (continued)
HAND/FINGER GEOMETRY READERS
• The first modern biometric device was a hand geometry reader that measured finger length
• These devices use a 3D or stereo camera to map images of the hands and/or fingers to measure size, shape and translucency
• Actual sensor devices are quite large in size• Templates are typically small (approx 10 Bytes)• High acceptance rate among users
SIGNATURE RECOGNITION
How the signature was made. i.e. changes in speed, pressure and timing that occur during the act of signing An expert forger may be able to duplicate what a signature looks like, but it is virtually impossible to duplicate the timing changes in X, Y and Z (pressure)
SIGNATURE ANALYSIS (continued)
•Built-in sensors register the dynamics of the act of writing. These dynamics include the 3D-forces that are applied, the speed of
writing, and the angles in various directions.
•This signing pattern is unique for each individual, and thus allows for strong authentication. It also protects against fraud since it is
practically impossible to duplicate "how" someone signs.
• A multimodal biometric system uses the integration of biometric systems in order to meet stringent performance requirements. •Much more vital
to fraudulent technologies
TOKENS, SMART CARDS & BIOMETRIC
AUTHENTICATION SCHEMES
INTEGRATION IS ESSENTIAL
CONCLUSION
Once the exclusive preserve of sci-fi books and movies, biometrics now has to be considered as one of the many challenges of modern day management.
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