Fingerprint Based Male-Female Classification
Manish Verma and Suneeta Agarwal
Computer Science Department, Motilal Nehru National Institute of Technology
Allahabad Uttar Pradesh India
manishverma649@gmail.com, suneeta@mnnit.ac.in
Abstract. Male-female classification from a fingerprint is an important step in forensic science,
anthropological and medical studies to reduce the efforts required for searching a person. The
aim of this research is to establish a relationship between gender and the fingerprint using some
special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and
ridge width. Ahmed Badawi et. al. showed that male-female classification can be done correctly
upto 88.5% based on white lines count, RTVTR & ridge count using Neural Network as Classifier. We have used RTVTR, ridge width and ridge density for classification and SVM as classifier. We have found male-female can be correctly classified upto 91%.
Keywords: gender classification, fingerprint, ridge density, ridge width, RTVTR, forensic,
anthropology.
1 Introduction
For over centuries, fingerprint has been used for both identification and verification
because of its uniqueness. A fingerprint contains three level of information. Level 1
features contain macro details of fingerprint such as ridge flow and pattern type e.g.
arch, loop, whorl etc. Level 2 features refer to the Galton characteristics or minutiae,
such as ridge bifurcation or ridge termination e.g. eye, hook, bifurcation, ending etc.
Level 3 features include all dimensional attributes of ridge e.g. ridge path deviation,
width, shape, pores, edge contour, ridges breaks, creases, scars and other permanent
details [10].
Till now little work has been done in the field of male-female fingerprint classification. In 1943, Harold Cummnins and Charles Midlo in the book “Fingerprints, Palm
and Soles” first gave the relation between gender and the fingerprint. In 1968, Sarah
B Holt, Charles C. Thomas in the book “The Genetics of the Dermal Ridges” gave
same theory with little modification. Both state the same fact that female ridges are
finer/smaller and have higher ridge density than males. Acree showed that females
have higher ridge density [9]. Kralik showed that males have higher ridge width [6].
Moore also carried out a study on ridge to ridge distance and found that mean distance
is more in male compared to female [7]. Dr. Sudesh Gungadin showed that a ridge
count of ≤13 ridges/25 mm2 is more likely to be of males and that of ≥14 ridges/25
mm2 is likely to be of females [2]. Ahmed Badawi et. al. showed that male-female
can be correctly classified upto 88.5% [1] based on white lines count, RTVTR &
ridge count using Neural Network as Classifier. According to the research of
E. Corchado et al. (Eds.): CISIS 2008, ASC 53, pp. 251–257, 2009.
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Cummnins and Midlo, A typical young male has, on an average, 20.7 ridges per centimeter while a young female has 23.4 ridges per centimeter [8].
On the basis of studies made in [6], [1], [2], ridge width, RTVTR and ridge density
are significant features for male-female classification. In this paper, we studied the
significance of ridge width, ridge density and ridge thickness to valley thickness ratio
(RTVTR) for the classification purpose. For classification we have used SVM classifier because of its significant advantage. Artificial Neural Networks (ANNs) can
suffer from multiple local minima, the solution with an SVM is global and unique.
Unlike ANNs, the computational complexity of SVMs does not depend on the dimensionality of the input space. ANNs use empirical risk minimization, whilst SVMs use
structural risk minimization. SVMs are less prone to overfitting [13].
2 Materials and Methods
In our Male-Female classification analysis with respect to fingerprints, we extracted
three features from each fingerprint. The features are ridge width, ridge density and
RTVTR. Male & Female are classified using these features with the help of SVM
classifier.
2.1 Dataset
We have taken 400 fingerprints (200 Male & 200 Female) of indian origin in the age
group of 18-60 years. These fingerprint are divided into two disjoint set for training
and testing, each set contains 100 male and 100 female fingerprints.
2.2 Fingerprint Feature Extraction Algorithm
The flowchart of the Fingerprint Feature Extraction and Classification Algorithm is
shown in Fig. 1. The main steps of the algorithm are:
Normalization [4]
Normalization is used to standardize the intensity values of an image by adjusting the
range of its grey-level values so that they lie within a desired range of values e.g. zero
mean and unit standard deviation. Let I(i,j) denotes the gray-level value at pixel (i,j),
M & VAR denote the estimated mean & variance of I(i,j) respectively & N(i,j) denotes the normalized gray-level value at pixel (i,j). The normalized values is defined
as follows:
(1)
Where M0 and VAR0 are desired mean and variance values respectively.
Image Orientation [3]
Orientation of a fingerprint is estimated by the least mean square orientation estimation algorithm given by Hong et. al. Given a normalized image, N, the main steps of
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Fig. 1. Flow chart for Fingerprint Feature Extraction and Classification Algorithm
the orientation estimation are as follows: Firstly, a block of size wXw (25X25) is
centred at pixel (i, j) in the normalized fingerprint image. For each pixel in this block,
compute the Gaussian gradients ∂x(i, j) and ∂y(i, j), which are the gradient magnitudes in the x & y directions respectively. The local orientation of each block centered
at pixel (i, j) is estimated using the following equations [11].
(2)
(3)
(4)
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where θ(i,j) is the least square estimate of the local orientation at the block centered
at pixel (i,j). Now orient the block with θ degree around the center of the block, so
that the ridges of this block are in vertical direction.
Fingerprint Feature Extraction
In the oriented image, ridges are in vertical direction. Projection of the ridges and
valleys on the horizontal line forms an almost sinusoidal shape wave with the local
minima points corresponding to ridges and maxima points corresponds to valleys of
the fingerprint.
Ridge Width R is defined as thickness of a ridge. It is computed by counting the
number of pixels between consecutive maxima points of projected image, number of
0’s between two clusters of 1’s will give ridge width
e.g.
11110000001111
in above example, ridge width is 6 pixels.
Valley Width V is defined as thickness of valleys. It is computed by counting the
number of pixels between consecutive minima points of projected image, number of
1’s between two clusters of 0’s will give valley width
e.g.
00001111111000
in above example, valley width is 7 pixels.
Ridge Density is defined as number of ridges in a given block.
e.g.
001111100011111011
Above string contains 3 ridges in a block. So ridge density is 3.
Ridge Thickness to Valley Thickness Ratio (RTVTR) is defined as the ratio of
ridge width to the valley width and is given by RTVTR = R/V.
Fig. 2. Segmented image is Oriented and then projected to line from its binary transform we got
ridge and valley width
Example 1. Fig. 2. shows a segment of normalized fingerprint, which is oriented so
that the ridges are in vertical direction. Then these ridges are projected on horizontal
line. In the projected image, black dots show a ridge and white show a valley.
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Classification
SVM’s are used for classification and regression. SVM’s are set of related supervised
learning methods. A special property of SVMs is that they simultaneously minimize
the empirical classification error and maximize the geometric margin; hence they are
also known as maximum margin classifiers. For a given training set of instance-label
pairs (Xi, yi), i=1..N, where N is any integer showing number of training sample, Xi
∈Rn (n denotes the dimension of input space) belongs to the two separating classes
labeled by yi∈{-1,1}, This classification problem is to find an optimal hyperplane WTZ
+ b =0 in a high dimension feature space Z by constructing a map Z=φ(X) between Rn
and Z. SVM determines this hyperplane by finding W and b which satisfy
(5)
.
Where ξ i ≥ 0 and yi[W φ(Xi)+b] ≥ 1- ξ i holds. Coefficient c is the given upper bound
and N is the number of samples. The optimal W and b can be found by solving the
dual problem of Eqn. (5), namely
T
(6)
.
Where 0 ≤ αi ≤ c (i = 1,….,N) is Lagrange multiplier and it satisfies
0.
) and we adopt the RBF function to map the input vecLet
tors into the high dimensional space Z. The RBF Function is given by
.
(7)
Where γ= 0.3125,c=512.Values of c & γ are computed by grid search [5]. The decision function of the SVM classifier is presented as
(8)
Where K(.,.) is the kernel function, which defines an inner product in higher dimension space Z and it satisfies that
. The decision function sgn(φ) is the sign function and if φ≥0 then sgn(φ)=1 otherwise sgn(φ)=-1 [12].
3 Results
Our experimental result showed that if we consider any single feature for Male–
Female classification then the classification rate is very low. Confusion matrix for
Ridge Density (Table 1), RTVTR (Table 2) and Ridge Width (Table 3) show that
their classification rate is 53, 59.5 and 68 respectively for testing set. But by taking all
these features together we obtained the classification rate 91%. Five fold cross validation is used for the evaluation of the model. For testing set, Combining all these features together classification rate is 88% (Table 4).
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M. Verma and S. Agarwal
Table 1. Confusion Matrix for Male-Female classification based on Ridge Density only for
Testing set
Actual\Estimated
Male
Female
Total
Male
47
41
88
Female
53
59
112
Total
100
100
200
For Ridge Density the classification rate is 53%
Table 2. Confusion Matrix for Male-Female classification based on RTVTR only for Testing set
Actual\Estimated
Male
Female
Total
Male
30
11
41
Female
70
89
159
Total
100
100
200
For RTVTR the classification rate is 59.5%
Table 3. Confusion Matrix for Male-Female classification based on Ridge Width only for
Testing set
Actual\Estimated
Male
Female
Total
Male
51
15
66
Female
49
85
134
Total
100
100
200
For Ridge Width the classification rate is 68%
Table 4. Confusion Matrix for Male-Female classification based on combining Ridge Density,
Ridge Width and RTVTR only for Testing set
Actual\Estimated
Male
Female
Total
Male
86
10
96
Female
14
90
104
Total
100
100
200
For Testing set the classification rate is 88%
4 Conclusion
Accuracy of our model obtained by five fold cross validation method is 91%. Our
results have shown that the ridge density, RTVTR and Ridge Width gave gave 53%,
59.5% and 68% classification rates respectively. Combining all these features together, we obtained 91% classification rate. Hence, our method gave 2.5 % better
result than the method given by Ahmed Badawi et al.
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