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FINGERPRINT
RECOGNITION
PROFESSOR SHAHRAM
TAHERI
Made By:
Muhammad Nouman Abbasi
Mirza Taimoor Sultan Baig
FINGERPRINT RECOGNITION
• Fingerprint recognition is one of the
oldest and most researched fields of
biometrics.
• The configuration types are
individually variable, but they vary
within limits that allow for a systematic
classification.
indiamart.com
FINGERPRINT FORMATION
Finger ridge
configurations
do not change
throughout the
life of an
individual
except.
aceproject.org
FINGERPRINT SENSORS
• Optical
• Silicon Based Capacitive Sensors
• Ultrasound
• Thermal
OPTICAL SENSORS
• An optical sensor converts light rays into an electronic signal.
• Optical Sensors are used for contact-less detection, counting or positioning of parts.
• Optical sensors can be either internal or external.
• The measurands possible by different optical sensors are Temperature, Velocity Liquid level,
Pressure, Displacement.
TYPES OF OPTICAL
SENSORS
There are different kinds of
optical sensors.
• Photoconductive
• Photovoltaic cell
• Photodiodes
ADVANTAGES
No reference electrode is needed.
No electrical interference.
Can easily be replaced.
No electrical safety hazards.
Multi wavelength measurements.
Potential for higher information content.
DRAWBACKS
Appropriate reagent phases can be developed.
Background ambient light.
Limited dynamic range.
Response times may be slow.
SILICON BASED
SENSORS
Its introduction in the late 90’s.
Based on DC Capacitance, but some also use AC Capacitance.
One plate of a capacitor, and the finger is the other.
8-bit grayscale digital image.
ULTRASOUND SENSORS
The most accurate of the
fingerprint technologies.
Transmitted ultrasound waves
and measures the distance.
This is a technology with
significant promise.
ADVANTAGES
Capable of penetrating dirt and residue on the sensing plate.
Overcomes the drawbacks of optical devices.
Ability to overcome sub-optimal reading conditions.
Virtually impossible to deceive an ultrasound system.
DRAWBACKS
Equipment expense.
Many cancers cannot be detected via an ultra sound.
Not available everywhere.
Preventing early diagnosis of the portion of breast cancer.
THERMAL SENSORS
• A temperature sensor is a device used to measure
temperature.
• Thermocouples, RTDs, thermistors, and semiconductor
based ICs/.
ADVANTAGES
 Electrostatic discharge.
 Temperature conditions as at room temperature.
 Deceive with artificial fingertips.
DRAWBACKS
 Image disappears quickly.
 The finger and the pixel array have reached thermal equilibrium.
 This can be avoided by using a scanning method but only a few
pixels high.
PRINCIPLES OF
FINGERPRINT
ANALYSIS
The fingerprint pattern, such
as the print left when an
inked finger is pressed onto
paper, is that of the friction
ridges on that finger. Friction
ridge patterns are grouped
into three distinct types
LOOPS
• Recurve back on themselves to form a loop
shape.
• Loops account for approximately 60 percent of
pattern types.
WHORLS
• Form circular or spiral patterns, like tiny whirlpools.
• There are four groups of whorls.
1. Plain
2. Central pocket loop
3. Double loop
4. Accidental loop
• Whorls make up about 35 percent of pattern types
ARCHES
• Create a wave-like pattern.
• Arches make up about 5 percent of all
pattern types.
LINE TYPES CLASSIFICATION
Bifurcation
Arch
Loop
Island
Ellipse
Tented Arch
Spiral
Rod
Sweat Gland
AUTOMATIC
VERIFICATION
SYSTEM
FEATURE EXTRACTION
• The human fingerprint is comprised of
various types of ridge patterns.
• Loops make up nearly 2/3 of all fingerprints,
whorls are nearly 1/3, and perhaps 5-10%
are arches
FEATURE
ENHANCEMENT
• The first step is to obtain a clear image of
the fingerprint.
• To improve the clarity of ridge and furrow
structures of input fingerprint
• Areas lighter than a particular threshold
are discarded
• Ridges are then thinned from 5-8 pixels
Original
Enhanced
TEMPLATE SELECTION
• Matching accuracy of a biometrics-based
authentication system relies on the stability.
• Biometric data acquired from an individual
is susceptible to changes introduced due to
improper interaction with the sensor
• The stored template data to be significantly
different from those obtained during
authentication
Variation in fingerprint exhibiting partial overlap.
MATCHING ALGORITHM
• Automatic Minutiae Detection
ALGORITHM:
• Compare the minutiae on the two images.
• The figure alongside is the input given to the system.
• The next step in the algorithm is to mark all the
minutiae points on the image.
• Image is superimposed onto the input image.
• Finally a comparison is made with the images
in the database.
APPLICATIONS
• Banking security - ATM security card
transaction
• Physical access control (e.g., Airport)
• Information system security
• National ID systems
• Prisoner, prison visitors
• Identification of criminals
• Identification of missing children
• Secure e-commerce (still under
research)
THANK YOU

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Fingerprint recognition

  • 2. FINGERPRINT RECOGNITION • Fingerprint recognition is one of the oldest and most researched fields of biometrics. • The configuration types are individually variable, but they vary within limits that allow for a systematic classification. indiamart.com
  • 3. FINGERPRINT FORMATION Finger ridge configurations do not change throughout the life of an individual except. aceproject.org
  • 4. FINGERPRINT SENSORS • Optical • Silicon Based Capacitive Sensors • Ultrasound • Thermal
  • 5. OPTICAL SENSORS • An optical sensor converts light rays into an electronic signal. • Optical Sensors are used for contact-less detection, counting or positioning of parts. • Optical sensors can be either internal or external. • The measurands possible by different optical sensors are Temperature, Velocity Liquid level, Pressure, Displacement.
  • 6. TYPES OF OPTICAL SENSORS There are different kinds of optical sensors. • Photoconductive • Photovoltaic cell • Photodiodes
  • 7. ADVANTAGES No reference electrode is needed. No electrical interference. Can easily be replaced. No electrical safety hazards. Multi wavelength measurements. Potential for higher information content.
  • 8. DRAWBACKS Appropriate reagent phases can be developed. Background ambient light. Limited dynamic range. Response times may be slow.
  • 9. SILICON BASED SENSORS Its introduction in the late 90’s. Based on DC Capacitance, but some also use AC Capacitance. One plate of a capacitor, and the finger is the other. 8-bit grayscale digital image.
  • 10. ULTRASOUND SENSORS The most accurate of the fingerprint technologies. Transmitted ultrasound waves and measures the distance. This is a technology with significant promise.
  • 11. ADVANTAGES Capable of penetrating dirt and residue on the sensing plate. Overcomes the drawbacks of optical devices. Ability to overcome sub-optimal reading conditions. Virtually impossible to deceive an ultrasound system.
  • 12. DRAWBACKS Equipment expense. Many cancers cannot be detected via an ultra sound. Not available everywhere. Preventing early diagnosis of the portion of breast cancer.
  • 13. THERMAL SENSORS • A temperature sensor is a device used to measure temperature. • Thermocouples, RTDs, thermistors, and semiconductor based ICs/.
  • 14. ADVANTAGES  Electrostatic discharge.  Temperature conditions as at room temperature.  Deceive with artificial fingertips.
  • 15. DRAWBACKS  Image disappears quickly.  The finger and the pixel array have reached thermal equilibrium.  This can be avoided by using a scanning method but only a few pixels high.
  • 16. PRINCIPLES OF FINGERPRINT ANALYSIS The fingerprint pattern, such as the print left when an inked finger is pressed onto paper, is that of the friction ridges on that finger. Friction ridge patterns are grouped into three distinct types
  • 17. LOOPS • Recurve back on themselves to form a loop shape. • Loops account for approximately 60 percent of pattern types.
  • 18. WHORLS • Form circular or spiral patterns, like tiny whirlpools. • There are four groups of whorls. 1. Plain 2. Central pocket loop 3. Double loop 4. Accidental loop • Whorls make up about 35 percent of pattern types
  • 19. ARCHES • Create a wave-like pattern. • Arches make up about 5 percent of all pattern types.
  • 22. FEATURE EXTRACTION • The human fingerprint is comprised of various types of ridge patterns. • Loops make up nearly 2/3 of all fingerprints, whorls are nearly 1/3, and perhaps 5-10% are arches
  • 23. FEATURE ENHANCEMENT • The first step is to obtain a clear image of the fingerprint. • To improve the clarity of ridge and furrow structures of input fingerprint • Areas lighter than a particular threshold are discarded • Ridges are then thinned from 5-8 pixels Original Enhanced
  • 24. TEMPLATE SELECTION • Matching accuracy of a biometrics-based authentication system relies on the stability. • Biometric data acquired from an individual is susceptible to changes introduced due to improper interaction with the sensor • The stored template data to be significantly different from those obtained during authentication Variation in fingerprint exhibiting partial overlap.
  • 25. MATCHING ALGORITHM • Automatic Minutiae Detection ALGORITHM: • Compare the minutiae on the two images. • The figure alongside is the input given to the system.
  • 26. • The next step in the algorithm is to mark all the minutiae points on the image. • Image is superimposed onto the input image. • Finally a comparison is made with the images in the database.
  • 27. APPLICATIONS • Banking security - ATM security card transaction • Physical access control (e.g., Airport) • Information system security • National ID systems • Prisoner, prison visitors • Identification of criminals • Identification of missing children • Secure e-commerce (still under research)