Classification of Fingerprints
Classification of Fingerprints
Classification of Fingerprints
Sarat C. Dass
Department of Statistics & Probability
Fingerprint Classification
Fingerprint classification is a coarse level partitioning
of a fingerprint database into smaller subsets.
The Henry system with five classes are shown above. The five
classes can be reduced to four by combining the PA and TA
classes to form the Arch (A) class. The natural frequencies of
W, L, R and A (A + T) are 27.9%, 33.8%, 31.7% and 6.6%.
The Henry Classification System (cont.)
• The five main classes differ in terms of the global flow
patterns of the ridge curves.
• They also differ in terms of the number and locations of
singular points in the fingerprint image. For example,
¾ LL – exactly one core and one delta; the core is to the left of the
delta,
¾ RL – exactly one core and one delta; the core is to the right of the
delta,
¾ W – two cores and two deltas,
¾ PA – no singular points, and
¾ TA – one core and one delta; the delta is approximately directly
below the core.
Problems with the Henry classification system: (i) non-
uniform classification proportions, and (ii) experts classify
some fingerprint images into different Henry classes.
Examples of such fingerprints are…
1. The orientation field (flow direction of the ridges at each site in the
fingerprint image) is extracted and smoothed.
2. Singular points are detected using the Poincare index. The Poincare
index is computed by summing the changes in the angles of flow in a
small circle around the test point. It is 0, -π, π, and 2π for regular,
delta, core and double core points, respectively.
Core
Delta
0 1 2
1. If N=1, consider the straight line joining the core and the delta. If N=2,
consider the straight line joining the two cores. Call this line L.
2. For tented arch (whorl), the tangent direction of L is parallel to the local
orientation values, but not so for loops (twin loop).
Structure based approaches
Structure based approaches use global characteristics of the ridges to
determine the fingerprint class.
Chang & Fan (2002) use ridge distribution models to determine the
class of the fingerprint.
(i) Handling ridge bifurcations (i) Handling ridge fragmentations (a), true
ridge endings (b).
Chang & Fan (2002), cont.
2. Each extracted ridge is then classified into one of the 10 basic
ridge patterns. Some examples of the classification are:
Chang & Fan (2002), cont.
3. The ridge distribution sequence is generated according to the
picture below:
•A. K. Jain, S. Prabhakar and L. Hong, " A Multichannel Approach to Fingerprint Classification", IEEE Transactions
on PAMI, Vol.21, No.4, pp. 348-359, April 1999.
192-dimensional Feature Vector
Two-stage classifier
• K-nearest neighbor classifier
• Neural Network classifier
Input Data
The best fitting kernel (the one that minimizes the energy
functional below a certain threshold) is taken to be the
class of the fingerprint image. Experimental results based
on the NIST4 database yields a classification accuracy of
91.25% for the 4 class problem.
Why Another Fingerprint Classifier ?
Dass & Jain (2004)
Limitations of the existing approaches:
Estimate
Input Fingerprint Generate OFFCs
Orientation Field
*S. C. Dass, “Markov Random Field Models for Directional Field and
Singularity Extraction in Fingerprint Images”, IEEE Transactions on
Image Processing, October 2004
Generation of Orientation Field Flow
Curves (OFFCs)
1 1
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
cos γ
cos γ
0 0
−0.2 −0.2
−0.4 −0.4
−0.6 −0.6
−0.8 −0.8
−1 −1
10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100
j j
Tangent Space Maps of Right-Loop
1 1
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
cos γ
cos γ
0 0
−0.2 −0.2
−0.4 −0.4
−0.6 −0.6
−0.8 −0.8
−1 −1
1 1
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
cos γ
cos γ
0 0
−0.2 −0.2
−0.4 −0.4
−0.6 −0.6
−0.8 −0.8
−1 −1
20 40 60 80 100 120 140 160 180 200 20 40 60 80 100 120 140 160 180 200 220
j j
Tangent Space Maps of Arch
1 1
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
cos γ
cos γ
0 0
−0.2 −0.2
−0.4 −0.4
−0.6 −0.6
−0.8 −0.8
−1 −1
10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 100 110
j j
Tangent Space Isometries of OFFCs
Assigned Class
True
class A L R W total accuracy