This document provides an overview of fingerprint recognition. It discusses key terms like minutiae points and fingerprint features. It describes the four main stages of fingerprint recognition: acquisition, preprocessing, feature extraction, and matching. The preprocessing stage enhances images through techniques like binarization and thinning. Feature extraction locates ridges, valleys and minutiae. The matching stage compares input fingerprints to templates to verify or identify individuals. The document provides details on minutiae, fingerprint patterns, and the verification and identification processes used in fingerprint recognition systems.

1. Fingerprint Recognition
NAME: TUFAIL AHMED
RED NO: 2016-KIU-1177
KARKURAM INTERNATIONAL UNIVERSITY GILGIT
BALTISTAN

2. Overviews
 Fingerprint
 Terms and definitions of fingerprint Structure
 Minutiae
 Fingerprint Features
 Fingerprint Classification
 Steps of Fingerprint Recognition
 Fingerprint Recognition Stages
1. Acquisition Stage
2. Pre-processing stage
3. Feature Extraction Stage
4. Matching Stage

3. Fingerprint
 Fingerprint is the primary part of biometrics
 Fingerprints are graphical patterns of ridges and
valleys on the surface of fingertips, the ridge ending
and ridge bifurcation is called minutiae

4. Cont….
 The fingerprint identification is based on two basic
assumptions:
 - Invariance and Singularity
 Invariance: means the fingerprint characteristics do
not change along the life.
 Singularity: means the fingerprint is unique and no
two persons have the same pattern of fingerprint.

5. Minutiae
 The unique features of a ridge, from which different
pattern occurs are called minutiae.
 Types of Minutiae
1. Ridge ending: where ridge terminates or discontinue.
2. Ridge bifurcation: is a feature where a
ridge splits or diverges
 From ridge ending and bifurcation, we can define
several other features.

6.  A fingerprint is defined by a set of minutiae point’s for example
ridge lines and they run parallel and sometimes starting , terminates,
sometimes intersects and move with one direction or opposite
direction. Here all points are known as Minutiae point

7. Fingerprint Features
 Level-1 General ridge flow and patterns
 Level 1 includes the overall pattern formed by the flow of
ridges, classification, ridge count, focal areas and orientation on
the surface of the finger
 Level-2 Ridge ending, dots and bifurcation (known as minutiae)
 Major ridge path variations, also known as minutiae. The
location of the major changes in individual ridges such as
ending, bifurcations, islands, dots, combinations, and their
relationships
 Level-3 Ridge contour points and pores
 Level 3 includes all dimensional attributes of a ridge such as
ridge width, pore patterns, path deviation, edge counter,
incipient ridges, and breaks.

8. Fingerprint Characteristics-> L1
 Arch
 In Arch, pattern ridges start from one side of the
fingerprint pattern to another side without doing
backward turn
 Whorl
 Whorl pattern consists of series of circles which starts
from an arbitrary point and ends at the same point
 Loop
 recursive ridges

9. Fingerprint Characteristics->
L2
 A fingerprint has several features, which are
 Island: a line that runs or flows alone without touching other lines or
regions
 Dot: an independent ridge which looks like a dot and equal in length
and width
 bridge or crossover: a small ridge which connects two parallel ridges
 Core: centre of the fingerprint pattern
 Delta: a point from which fingerprint pattern alters or deviates

10. Steps of Fingerprint
Recognition
 Enrollment: The first step to do fingerprint recognition is
enrollment which is the process to register the biometric
data to database as a template then fingerprint
recognition undergo either Verification process or
Identification process.
 Verification: In the verification process the person’s
fingerprint is verified from the database by using
matching algorithms. Also it is called (1:1) Matching. It is
the comparison of a claimant fingerprint against enroll
fingerprint, initially the person enrolls his/her fingerprint
into verification system, and the result show whether the
fingerprint which take from the user is matching with the
fingerprint store as a template in database or not match.

11. Cont…
 Identification: fingerprint acquired from one person is compared
with all the fingerprints which store in database. Also it is called
(1:N) matching.

12. Fingerprint Recognition
Stages
 The Fingerprint Recognition system is divided into
four stages
1. Acquisition Stage
2. Pre-processing stage
3. Feature Extraction Stage
4. Matching Stage

13. Acquisition Stage
 to capture the fingerprint image, by different ways
such as Online and Offline
 Online: Used Optical fingerprint reader.
 Offline: the fingerprint image is obtained by ink in the
area of finger and then put a sheet of white Paper on
fingerprint and scan it to get a digital image

14. Preprocessing Stage
 is the process of removing
unwanted data in fingerprint image
such as noise , reflection ,etc.
 It is used to increase the
clarity of ridge structure.
 The main steps to do
preprocessing are
enhancement , binarization
and thinning.

15. Processing stage:
Enhancement
 For fingerprint enhancement we applied the following
steps:
 •Identify ridge segment/normalization
 •Determine ridge orientations
 •Determine ridge frequency
 •Apply filters
 •Histogram Equalization
 •FFT Enhancement

16. Processing stage: Binarization
 is a process to transform the image from 256 levels to two
levels(0,1)refers to (black and white).
 or the process of converting an 8-bit gray-scale fingerprint image
into a 1-bit ridge image.

17. Processing stage: Thinning
 Also called skeletonization.
 To enhance the binary image the thinning algorithm is
used to reduce the ridges of fingerprint images.
 The most popular thinning algorithms are
 media axis method, contour generation method,
sequential and parallel thinning

18. Processing stage: Thinning

19. Feature Extraction Stage
 The result of pre-processing stage is passed
to the feature extraction.
 In this stage feature of image are extracted
like ridges, valleys, minutiae, singular points
and etc.
 These features are used for verification and
identification.
 to extract the feature from the thinning image by use
minutiae extractor methods to extract ridge ending and ridge
bifurcation from thinning
 store the ridge endings and ridge Bifurcation in matlab file.

20. Matching( Identification,
Verification)
 The matching stage is a process to compare two
fingerprints images(input and template ) and
compute the similarity degree between them.
 The FP matching performance is evaluated by
means of FAR, FRR, EER (Equal Error Rate), GAR
(Genuine Accept Rate, GAR= 1- FRR).
 Pattern Matching: compares two image for
checking similarity.
 Minutiae based Matching: relies on location
and direction of minutiae points.

21. Matching( Identification, Verification)
 Identification process
 It is the process for comparing between the user of biometric data and
multiple users of template data which take at enrollment phase
 also known as (1:N) matching
 Verification process
 It is the process of comparison between the user of biometric data and
one template
 also is known as (1:1)matching

22. Thank
You!!