Embed Size (px)
DESCRIPTION
TITRE. Parametric colour transformation: (a) genuine and (b) fake samples. (a). (b). Classification capability of individual (blue line) features and their gradual combination (brown line). Holes in character images: (a) a genuine and (b) a fraudulent samples. - PowerPoint PPT Presentation
Citation preview
TITRETITRE
Conseil Scientifique, 4 Mai 2007 ICVGIP 2010 contact : [email protected]
Authentication of Currency Notes through Printing Technique VerificationAnkush Roy1, Biswajit Halder2, Utpal Garain3
1 Student, Dept. of Elect. Engg Jadavpur University,Kolkata,India2 Mallabhum Institute of Technology, Bishnupur, WB, India
3 Indian Statistical Institute, Kolkata,India
A new method to detect counterfeit paper currency
The method is based on verifying the printing process used (Intaglio printing) as a security feature Two classifiers are used: Neural Network & SVMThe discriminatory power of the features is shown using LDA This algorithm is evaluated on real data
IntroductionIntroduction
• Dominant intensity
Features
Experimentation
• Hole Count
• Average Hue
•Contrast
RMS contrast is used to tap the slight difference
in brightness (or glossiness) that the human eye fails to
recognise
•Keytone
The mean gray value of all the pixels. The value of key tone indicates whether
the bulk of information in an image is stored in the high/middle/low intensity zone
•Correlation Coefficient:A is the grayscale image and B the
corresponding binary image after
adding a normalized parameter (sc/255)
over the Otsu limit
• Average Color
A colour transformation is made on two indivisual colour streams to produce
a new colour matrix
•Perimeter Based Edge Roughness:
• pa is the perimeter of the actual image
• pb is perimeter of the filtered binary image
• EPBER is the perimeter based edge roughness
•Area difference:
• Area difference = │(Aotsu+sc – Aotsu ) │/Aotsu
• A is the binarised image with a normalised parameter (sc)
(a) (b)Holes in character images: (a) a genuine and (b) a fraudulent samples
Histogram of hue of character strokes: (a) genuine and (b) fake sample
N
ii II
N 1
2
1
1
# Support Vec.
# Iterations Weight Vector |w|
% accuracy MSE
Poly RBF Poly RBF Poly RBF Poly RBF Poly RBF
Run-1 3 25 11 16 1.0597 4.0832 100 100 1.297 0.129
Run-2 6 24 4 10 1.1435 4.188 100 99 1.576 0.132
Run-3 4 23 3 10 0.8056 4.1843 100 99.5 1.044 0.154
Run-4 4 23 6 13 0.9311 4.28 99.5 100 1.354 0.112
Avg. 4.25 24 6 12.3 0.9852 4.1839 99.9 99.6 1.318 0.134
Classification capability of individual (blue line) features and their gradual combination (brown line).
NN- based Classification too achieves very high accuracy (about 99.5%, 0.5% error is attributed to true negative)
Classification of Currency Note Printing Techniques Using SVM
Figure 1. Role of dominant intensity.
Figure (c) and (d) show the masked images of two character images extracted from two currency notes (one genuine and one fake respectively). Figures (e) and (f) show the histograms of gray levels as computed on the masked images.
Iterations Distribution of Sample in Clusters Clustering accuracy
(%)= (g1+d2)/2
#samples in genuine (G)
#samples in duplicate (D)
#G(g1) #D(d1) #D(d2) #G(g2)
1 95 9 91 5 93%
2 93 11 89 7 91%
3 95 7 93 7 94%
Avg. 94.3 9 91 5.7 92.7%Clustering of Currency Note Printing Techniques using K-Means
N
ii II
N 1
2
1
1
N
ii II
N 1
2
1
1
ROIji BjiBROIji AjiA
ROIjiBjiBAjiA
],[2],[],[
2],[
],[],[],[
bba pppE /)(PBER
Parametric colour transformation: (a) genuine and (b) fake samples.
(a) (b)
S(i) = pBblue (i) + (1 – p)Bblack(i) , 0<p<0.5
Holes in character images: (a) a genuine and (b) a fraudulent sample
