Multimodal Biometrics Recognition by Dimensionality Diminution Method

Suvarnsing Bhable Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 12, (Part - 2) December 2015, pp.122-124
www.ijera.com 122|P a g e
Multimodal Biometrics Recognition by Dimensionality
Diminution Method
Suvarnsing Bhable
(Research Student Department.of Computer Science & Information Technology,
Dr. Babasaheb Ambedkar Marathwada University, Aurangabad)
ABSTRACT
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
Keywords: PCA, Multimodal biometrics, Dimensionality Diminution, Face & Palmprint recognition.
I. INTRODUCTION
Biometrics is an emerging technology [1] that is
used to identify people by their physical and/or
behavioral characteristics and, so, inherently requires
that the person to be identified is physically present
at the point of identification. The physical
characteristics of an individual that can be used in
biometric identification/verification systems are
fingerprint, hand geometry, palm print [2], face [3],
iris, retina, and ear; the behavioral characteristics are
signature, lip movement, speech, keystroke
dynamics, gesture, and gait [1]. Biometric systems
based on a single biometric characteristic are referred
to as unimodal systems. They are usually more cost-
efficient than multimodal biometric systems.
However, a single physical or behavioral
characteristic of an individual can sometimes fail to
be sufficient for identification. For this reason,
multimodal biometric systems that integrate two or
more different biometric characteristics are being
developed to provide an acceptable performance, to
increase the reliability of decisions, and to increase
robustness to fraudulent technologies [4]. Hong et al.
[5] developed a prototype multimodal biometric
system, which integrates faces and fingerprints at the
identification stage. Ribaric and Fratric [6] presented
a multimodal biometric system based on features
extracted from fingerprint and palmprint data.
Recently, subspace methods, which select low
dimensional features to represent raw data, have been
widely studied in biometrics researches [7, 8]. It is
shown that subspace selection is one of the most
important steps for entire biometric systems. These
methods which carry out fusion at feature level
obtain the low-dimensional features integrated the
information of all modalities.
In this paper, we propose a new dimensionality
reduction method called Dimension Diminish
Projection (DDP) for multimodal biometric
recognition. The rest of this paper is organized as
follows: Section 2 describes the proposed algorithm
DDP. In Section 3, experimental results on the face
and palmprint databases are presented. We conclude
this paper in section 4.
II. DIMENSIONALITY DIMINISH
PROJECTION
To take face images as an example, there are two
kinds of face difference: intra-class face difference
and extra-class difference. The intra-class difference
contains transformation difference such as
illumination, pose changes while the extra-class
difference contains intrinsic difference (identity
changes), transformation difference.
In this section, DDP is proposed as a new
dimensionality reduction method, which can be used
to extract features from multimodal biometric data. It
modeling intra-class metric matrix to characterize the
distribution of transformation difference and extra-
class metric matrix to characterize the distribution of
intrinsic difference. Figure 1 shows the block
diagram of the proposed system for multimodal
biometrics system. It is well known that the manifold
or distribution of one face (or palm print) under
variations such as viewpoint, deformation, and
illumination is highly nonlinear and nonconvex [11].
However, we can recover the nonlinear structure by
locally linear fits [12]. We adopt linearization
extension of locally linear embedding (LLE) [13] to
model the intra- class metric matrix. LLE can
preserve the local geometry of the faces in the same
class. Because of the high-dimensional raw data, the
RESEARCH ARTICLE OPEN ACCESS

step of preprocessing is recommended for
compressing the raw data. In this work, we adopt a
straightforward image projection technique, Two-
Dimensional Principal Component Analysis
(2DPCA) [9] for initial dimension reduce.
2.1 Two-Dimensional Principal Component
Analysis
Two-dimensional principal component analysis
(2DPCA) is developed for image feature extraction.
As opposed to conventional PCA, 2DPCA is based
on 2D matrices rather than 1D vectors. That is, the
image matrix does not need to be previously
transformed into a vector. In contrast to the
covariance matrix of PCA, the size of the image
covariance matrix using 2DPCA is much smaller. As
a result, 2DPCA has two important advantages over
PCA. First, it is easier to evaluate the covariance
matrix accurately. Second, less time is required to
determine the corresponding eigenvectors. Let P
denote an n-dimensional unitary column vector. Our
idea is to project image A, an m´n random matrix,
onto P by the following linear transformation:
B = AP. (1)
Thus, we obtain an m-dimensional projected
vector B, which is called the projected feature vector
of image A. The projection vector P can be
determined by solving eigenvector of image
covariance (scatter) matrix Gt corresponding to the
largest eigenvalue [10],
Where G E[(A E(A))T (A E(A))] .We can
evaluate Gt directly using the training image
samples. Suppose that there are M training image
samples in total, the jth training image is denoted by
an m´n matrix Aj (j=1, 2.M), and the average image
of all training samples is denoted by A.
2.2 Two-Dimensional Principal Component
Analysis
Two-dimensional principal component analysis
(2DPCA) is developed for image feature extraction.
As opposed to conventional PCA, 2DPCA is based
on 2D matrices rather than 1D vectors. That is, the
image matrix does not need to be previously
transformed into a vector. In contrast to the
covariance matrix of PCA, the size of the image
covariance matrix using 2DPCA is much smaller. As
a result, 2DPCA has two important advantages over
PCA. First, it is easier to evaluate the covariance
matrix accurately. Second, less time is required to
determine the corresponding eigenvectors.
2.3 Modeling of intra-class metric matrix
Modalities, the images are compressed and
sorted by its feature vectors x. Denote the multimodal
biometric data set m n X x xN R = [ , , ]Î ´ 1r *K* r ,
and each datum xi r belongs to one class, each of
which has two or more modalities. The data X is the
training data set, on which the DDP algorithm is
developed. The system obtains a transformation
matrix U ÎRm´d that maps the set X of N points to
the set d n
Y y yN R = [ , , ]Î ´ 1rK r , such that Y=UTX, where
d<m.
Given the testing set P, we can project it to the
subspace via the transformation matrix U as Q=UTP.
Finally, we classify the dimension-reduced testing
data Q in the subspace by matching with the
corresponding training data Y.
III. EXPERIMENTS & RESULTS
In this section, the performance of DDP was
evaluated on the ORL face databases and the PolyU
palmprint database [14]. Our system can be extended
to include more modalities. There are 10 different
images of 40 distinct subjects contained in ORL
database. For some of the subjects, the images were
taken at different times, varying lighting slightly,
facial expressions and facial details.. The size of each
image is 92×112, 8-bit grey levels. The PolyU
palmprint database contains 7752 grayscale images
corresponding to 386 different palms. We select 25
individuals and for each individual select 10 face
images and 10 palmprint images, 5 for training and
the other 5 for testing. That is, we have 125 images
for training and 125 images for testing in one modal.
We compared DDP with the performances of other
methods including PCA [8], LDA, and MFA
[15][18]. In the preprocessing step, we take d=5 (5
projection vectors), for the face image size of
92×112, then the dimension after 2DPCA is reduced
to 560. The nearest extra-class neighbors parameter k
in DDP is chosen as k=4 and 5, so we can obtain 2
results DDP4 and DDP5. Table 1 gives the
recognition rates with the corresponding reduced
dimensions of the employed algorithms on ORL and
PolyU database. Figure 2 shows the average
recognition rates versus subspace dimensions in
multimodal tests. We can see that the proposed
algorithm DDP performed much better than other
algorithms. The performances of DDP4 and DDP5
are nearly the same.
We also take another experiment on the variance
of d (number of projection vectors). Table 2 gives the
recognition rates and recognition time.
Fig 1: Recognition results on the ORL and PolyU
database

Table 1: Recognition Results (%) On The Orl And
Polyu Dataset
Method Recognition
Rate (%)
Dimension (d)
PCA 73.6 400
LDA 83.2 200
MFA 89.6 350
DDP 93.6 30
DDP 95.2 30
Table 2: Recognition Results (%) Versus The
Number Of Projection Vectors On The Orl And
Polyu Data Set.
d Recognition
Rate (%)
Recognition
Time(s)
Dimension
3 78.2 5.41 100
5 93.6 9.65 30
10 95.8 26.34 30
IV. CONCLUSION
In this paper, a new dimensionality reduction
method called Dimension Diminish Projection (DDP)
is proposed for multimodal biometric recognition.
DDP preserves local information by capturing the
intra-modal geometry and extract between-class
relevant structures which is useful for classification.
Experimental results have shown the effectiveness
and the advantages of the proposed method.
REFERENCES
[1] Biometrics: Personal Identification in
Networked Society, A.K. Jain, R. Bolle, and
S. Pankanti, eds. Kluwer Academic, 1999.
[2] W. Shu and D. Zhang, ―Automated Personal
Identification by Palmprint,‖ Optical Eng.,
vol. 37, no. 8, pp. 2359-2362, Aug. 1998.
[3] Intelligent Biometric Techniques in
Fingerprint and Face Recognition, L.C. Jain,
U. Halici, I. Hayashi, and S.B. Lee, eds.
CRC Press, 1999.
[4] A.K. Jain and A. Ross, ―Multibiometric
Systems,‖ Comm. ACM, vol. 47, no. 1, pp.
34-40, Jan. 2004.
[5] L. Hong, A.K. Jain, Integrating faces and
fingerprints for personal identification,
IEEE Trans. Pattern Anal. Mach. Intell. 20
(12) (1998) 1295–1307.
[6] S. Ribaric, I. Fratric, A biometric
identification system based on eigenpalm
and eigenfinger features, IEEE Trans.
Pattern Anal. Mach. Intell. 27 (11) (2005)
1698–1709.
[7] S. Yan, D. Xu, B. Zhang, H. Zhang, Q.
Yang, S. Lin, Graph embedding and
extensions: a general framework for
dimensionality reduction, IEEE Trans.
Pattern Anal. Mach. Intell. 29 (1) (2007)
40–51.
[8] Sangramsing N. Kayte , Suvarnsing G.
Bhable , Jaypalsing N. Kayte , Raju Maher "
A Review Paper on Multimodal Biometrics
System using Fingerprint and Signature"
International Journal of Computer
Applications (0975 – 8887) Volume 128 –
No.15, October 2015.
[9] M. Turk, A. Pentland, Eigenfaces for
recognition, Int. J. Cognitive Neurosci. 3 (1)
(1991) 71–86.
[10] Jian Yang, David Zhang, Alejandro F.
Frangi, and Jing-yu Yang. Two-
Dimensional PCA: A New Approach to
Appearance-Based Face Representation and
Recognition, IEEE TRANSACTIONS ON
PATTERN ANALYSIS AND MACHINE
INTELLIGENCE, VOL. 26, NO. 1,
JANUARY 2004.
[11] J. Yang, J.Y. Yang, ―From Image Vector to
Matrix: A Straightforward Image Projection
Technique—IMPCA vs. PCA,‖ Pattern
Recognition, vol. 35, no. 9, pp. 1997-1999.
[12] S.Z. Li, A.K. Jain, Handbook of Face
Recognition, Springer, New York, 2005.
[13] S. Roweis, L.K. Saul, Nonlinear
dimensionality reduction by locally linear
embedding, Science 290 (5500) (2000)
2323–2326.
[14] X. He, D. Cai, S. Yan, H.J. Zhang,
Neighborhood preserving embedding, in:
Proceedings of the IEEE International
Conference on Computer Vision, 2005, pp.
1208–1213.
[15] PolyU Palmprint Palmprint Database,
/http://www.comp.polyu.edu.hk/_biometrics
/S, 2006.
[16] S.C. Yan, D. Xu, B. Zhang, H.J. Zhang,
Graph embedding and extensions: a general
framework for dimensionality reduction,
IEEE
[17] Trans. Pattern Anal. Mach. Intell. 29 (1)
(2007) 40–51.
[18] Suvarnsing G. Bhable, Sangramsing Kayte,
Jaypalsing N. Kayte, Dr. Charansing
Kayte"Robust Multimodal Biometrics
Recognition: A Review‖ International
Journal of Advanced Research in Computer
Science and Software Engineering -Volume
5, Issue 10, October-2015

Multimodal Biometrics Recognition by Dimensionality Diminution Method

Recommended

More Related Content

What's hot (19)

Viewers also liked (20)

Similar to Multimodal Biometrics Recognition by Dimensionality Diminution Method (20)

Recently uploaded (20)

Multimodal Biometrics Recognition by Dimensionality Diminution Method