Fingerprints have long been used for identification and are considered a primary biometric technology. Fingerprints are unique to each individual and easy to use, cheap, small, and non-intrusive. Fingerprints are classified into loops, whorls, and arches, with loops making up 60-65% of fingerprints. Feature extraction involves enhancing ridges, extracting minutiae points like bifurcations and endings, and calculating coordinates and angles to allow for matching fingerprints in a database.

1. Fingerprint Technology
Biometrics:

2. Fingerprint Biometrics
An extremely useful biometrics technology
since fingerprints have long been recognized
as a primary and accurate identification
method.

3. Benefits of Fingerprint Biometrics
 Fingerprints are unique, with no two fingers
having the exact same dermal ridge
characteristics
 Easy to use
 Cheap
 Small size
 Low power
 Non-intrusive

4. Whorl Right Loop Left Loop Tented Arch Arch
Classification of Fingerprints
•Large volumes of fingerprints are being collected in everyday applications-for e.g.. The FBI database has 70
million of them.
•To reduce the search time and computational complexity classification is necessary.
•This allows matching of fingerprints to only a subset of those in the database.
•An input fingerprint is first matched at a coarse level to one of the pre-specified types and then, at a finer level,
it is compared to the subset of the database containing that type of fingerprints only.
•Numerous algorithms have been developed in this direction.
Fingerprint Classification

5. Types of Prints
 FINGERPRINTS HAVE GENERAL RIDGE PATTERNS FOR
CLASSIFICATION:
 Divided into three classes:
 LOOP
 WHORL
 ARCH
 60-65% OF POPULATION HAS LOOPS
 30-35% WHORLS
 AND 5% ARCHES.

6. Automatic Verification System

7. Fingerprint Extraction and Matching
Biometrics
Biometrics

8.  The human fingerprint is comprised of various types of ridge
patterns.
 Traditionally classified according to the decades-old Henry system:
left loop, right loop, arch, whorl, and tented arch.
 Loops make up nearly 2/3 of all fingerprints, whorls are nearly 1/3,
and perhaps 5-10% are arches.
 These classifications are relevant in many large-scale forensic
applications, but are rarely used in biometric authentication.
Feature Extraction

9. Advanced Minutiae Based Algo
 Feature Extractor
 Capture Image
 Enhance Ridge
 Extract Minutiae

10. Advanced Minutiae Based Algo
 Feature Extractor
 Most frequently used minutiae in
applications
 Points of bifurcation
 Ridge endings

11. Advanced Minutiae Based Algo
 Feature Extractor
 Minutiae Coordinate and Angle are calculated
 Core is used as center of reference (0,0)

12. Advanced Minutiae Based Algo
 Matcher
 Used to match fingerprint
 Trade-off between speed and performance
 Group minutiae and categorize by type
 Large number of certain type can result in faster searches

13. Identification vs. Authentication
 Identification – Who are you?
 1 : N comparison
 Slower
 Scan all templates in database
 Authentication – Are you John
Smith?
 1 : 1 comparison
 Faster
 Scan one template

14. Security
 Several sensors to detect fake fingerprints
 Cannot steal from previous user
 Latent print residue (will be ignored)
 Cannot use cut off finger
 Temperature
 Pulse
 Heartbeat sensors
 Blood flow

15. Applications

16. Applications

17. Versus other Biometric
Technologies