1. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 2, April 2012
DATA LEVEL FUSION FOR MULTI BIOMETRIC SYSTEM USING FACE
AND FINGER
Shubhangi Sapkal
Govt. College of Engineering, Aurangabad
 by multiple biometric sensors, algorithms, samples, units, or
Abstract— In this work, most commonly used and accepted traits. In addition to improving recognition accuracy, these
biometrics face and finger are used for data level fusion. systems are expected to improve population coverage, reduce
Multi biometric systems are expected to improve population spoofing and be resilient to fault tolerance of different mono
coverage, reduce spoofing and be resilient to fault tolerance modal biometric systems [5]. Face recognition is a
of different mono modal biometric systems. This system is nonintrusive method, and facial images are probably the most
designed for access control system requires more security common biometric characteristic used by humans to make
such as allow to access important data, it is the false personal recognition. It is questionable whether the face
acceptance rate that is major concern in such applications. itself, without any contextual information, is a sufficient
We do not want to access the data even the risk of manually basis for recognizing a person from a large number of
examining a large number of potential matches identified by identities with an extremely high level of confidence.
the biometric system. Humans have used fingerprints for personal identification
from many decades. But, fingerprints of a small fraction of
the population may be unsuitable for the automatic
Index Terms—Multi modal biometrics, Failure-to-enroll, identification because of genetic factors, aging,
Fusion environmental, or occupational reasons (e.g., manual
workers may have a large number of cuts and bruises on their
fingerprints that keep changing) [6] . The initial idea and
I. INTRODUCTION early work of this research have been published in part as
Multimodal biometric systems are those which conference papers in [7], [8], [9].
utilize, or have capability of utilizing, more than one The outline of the work is as follows. Section 2
physiological or behavioral characteristic for enrollment, discusses approaches presented in the literature. Section 3
verification, or identification. The reason for combining deals with image fusion. Section 4 extends to modes of
different sensor modalities is to improve the recognition operations. Section 5 discusses on Wavelet Transform and
accuracy [1]. Unimodal biometric systems have to contend Decomposition. Section 6 contains similarity Measures.
with a variety of problems such as noisy data, intra-class Experimental results are given in section 7. Finally
variations, restricted degrees of freedom, non-universality, conclusions are drawn in section 8.
spoof attacks, and unacceptable error rates. Some of these
limitations can be addressed by deploying multimodal
biometric systems that integrate the evidence presented by II. RELATED RESEARCH ON MULTIMODAL BIOMETRICS
multiple sources of information [2]. In [10], the data level fusion is used and the DWT
For IDs application, multimodality may be an coefficients are selected as features and the image is
effective tool to reduce the Failure to Enroll (FTE) rate. The reconstructed with those features. Miguel Carrasco in [11]
sequential use of multiple modalities guarantees that the proposed a bimodal identification system that combines face
non-enrollable population is reduced drastically. and voice information. A probabilistic fusion scheme at the
Furthermore, sequential use of modalities permits fair matching score level is used, which linearly weights the
treatment of persons that do not possess a certain biometric classification probabilities of each person-class from both
trait [3]. Here two inexpensive and widely accepted face and voice classifiers.
biometric traits namely face and fingerprint is used. Human In [12], histogram equalization of biometric score
face recognition has a tremendous potential in a wide variety distribution is successfully applied in a multimodal person
of commercial and law enforcement applications. verification system composed by prosodic, speech spectrum
Considerable research efforts have been devoted to the face and face information. Furthermore, a new bi-Gaussian
recognition problem over the past decade. Although there are equalization (BGEQ) is introduced. Stephen J. Elliott in [13],
a number of face recognition algorithms which work well in outlines the perceptions of 391 individuals on issues relating
constrained environments, face recognition is still an open to biometric technology. Results demonstrated
and very challenging problem in real applications [4]. overwhelming support for biometrics applications involving
Biometrics has long been known as a robust approach for law enforcement and obtaining passports, while applications
person authentication. However, most mono modal involving time and attendance tracking and access to public
biometrics are proven to exhibit one or more weaknesses. schools ranked lowest on the list.
Multi biometric systems combine the information presented A bimodal biometric verification system based on k-Nearest
Neighbourhood (k-NN) classifiers in the decision fusion
Manuscript received April 07, 2012. module for the face and speech experts is discussed in [14].
Shubhangi Sapkal, Computer Science and Engineering In [15], a method of speaker recognition is introduced based
Department,Government College of Engineering, Aurangabad., India.
80
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2. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 2, April 2012
on multimodal biometrics by using the kernel fisher biometric characteristics do not have to be acquired
discriminant analysis. Michał Chora [16] proposed a system simultaneously. Further, a decision could be arrived at
on the basis of ear, palm and lips images for human without acquiring all of the traits. This reduces the overall
identification. The combination of iris and fingerprint recognition time. In the hierarchical scheme, individual
biometrics is used in [17]. Jian Yang [18] proposed an classifiers are combined in a treelike structure. This work
unsupervised discriminant projection (UDP) technique for used parallel mode for fusion of face and finger.
dimensionality reduction of high dimensional data in small
sample size cases. UDP can be seen as a linear approximation V. WAVELET TRANSFORM
of a multimanifolds-based learning framework which takes
into account both the local and nonlocal quantities. The The biometrics image fusion extracts information
method is applied to face and palm biometrics and is from each source image and obtains the effective
examined using the Yale, FERET, and AR face image representation in the final fused image [29]. The
databases. aim of image fusion technique is to process the
M. K. Shahin, A. M. Badawi Proposed in [19] three
biometric modalities for validating and implementing fusing detailed information which obtains from
multimodal biometric system, that are hand vein, hand both the source images. The multi-resolution
geometry and fingerprint. image used to represent the signals where
decomposition is performed for obtaining finer
detail. Multi-resolution image decomposition gives
III. IMAGE FUSION
an approximation image and three other images
The three possible levels of fusion are: fusion at the feature viz., horizontal, vertical and diagonal images of
extraction or data level [20], fusion at the matching score
level [21] , [22], [23] [24], [25] and fusion at the decision
coarse detail. The face and fingerprint images are
level [26], [27], [28]. obtained from different sources. After re-scaling, the
(a) Fusion at the data or feature level: Either the data itself or images are fused by using wavelet transform and
the feature sets originating from multiple sensors/sources are decomposition. Finally, we obtain a completely
fused [2]. The data obtained from each sensor is used to new fused image, where both the attributes of face
compute a feature vector. As the features extracted from one
biometric trait are independent of those extracted from the
and fingerprint images are focused and reflected.
other, it is reasonable to concatenate the two vectors into a The proposed image fusion rule selects the larger
single new vector. The new feature vector now has a higher absolute values of the two wavelet coefficients at
dimensionality and represents a person‘s identity in a each point. Therefore, a fused image is produced
different hyperspace. Feature reduction techniques may be by performing an inverse wavelet transform based
employed to extract useful features from the larger set of
features.
on integration of wavelet coefficients correspond
(b) Fusion at the matching score level: Each system provides to the decomposed face and fingerprint images. More
a matching score indicating the proximity of the feature formally, wavelet transform decomposes an image
vector with the template vector. These scores can be recursively into several frequency levels and each
combined to assert the veracity of the claimed identity. level contains transform values.
(3) Fusion at the decision level: Each sensor can capture
Finally, inverse wavelet transformation is
multiple biometric data and the resulting feature vectors
individually classified into the two classes––accept or reject. performed to restore the fused image. The fused
A majority vote scheme can be used to make the final image possesses good quality of relevant
decision [22]. information for face and fingerprint images.
IV. MODES OF OPERATION In this work daubechies2 (Fig. 2) wavelet family
A multi biometric system can operate in one of three
for decomposition (Fig. 1) is used.
different modes: serial mode, parallel mode, or hierarchical
mode[6]. In the serial mode of operation, the output of one
biometric trait is typically used to narrow down the number
of possible identities before the next trait is used. This serves
as an indexing scheme in an identification system. For
example, a multi biometric system using face and fingerprints
could first employ face information to retrieve the top few
matches, and then use fingerprint information to converge
onto a single identity. This is in contrast to a parallel mode of
Fig. 1: Wavelet decomposition
operation where information from multiple traits is used
simultaneously to perform recognition. This difference is
crucial. In the cascade operational mode, the various
81
All Rights Reserved © 2012 IJARCSEE

3. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 2, April 2012
Threshold FRR FAR
0.6 0.14 0
0.65 0.32 0
0.7 0.44 0
0.75 0.56 0
0.8 0.75 0
0.85 0.87 0
Fig 2: A daubechies 2 wavelet Table 1: Recognition performance for different threshold
values using ‗MEAN‘ fusion technique
VI. EXPERIMENTAL EVALUATION
Threshold FRR FAR
A typical biometric recognition system commits two 0.6 0.34 0.21
types of errors: false acceptance and false 0.65 0.4 0.19
rejection; a distinction has to be made between 0.7 0.43 0.07
positive and negative recognition; in positive 0.75 0.61 0
recognition systems (e.g., an access control 0.8 0.79 0
0.85 0.88 0
system) a false match determines the false
Table 2: Recognition performance for different threshold
acceptance of an impostor, whereas a false values using ‗MAX-MIN‘ fusion technique
non-match causes the false rejection of a genuine
user. On the other hand, in a negative recognition
application (e.g., preventing users from obtaining
welfare benefits under false identities), a false
match results in rejecting a genuine request,
whereas a false non-match results in falsely
accepting an impostor attempt. The notation ―false
match/false non-match‖ is not application
dependent and therefore, in principle, is preferable
to ―false acceptance/false rejection.‖ However, the
use of false acceptance rate (FAR) and false Graph 1: - FAR-FRR diagram for MEAN method
rejection rate (FRR) is more popular and largely
used in the commercial environment [31]. Positive
recognition system is considered in this work and
correlation is used as similarity measure.
FRR is False Rejection Ratio, which means the
fault when someone which registered in the system
was refused by system [33]. Table I presents the
FRR values of genuine person faces.
FAR is False Acceptance Rate, which is the fault
where someone of user which does not enlist will Graph 2: - FAR-FRR diagram for MAX-MIN
be held true by the system. FAR values for method
impostor persons are presented in Table II.
Finally, Table III presents the FAR and FRR
VII. CONCLUSION
values for all persons with different threshold
values. The FRR and FAR for number of A 2D Discrete Wavelet Transform is proposed to
participants (N) are calculated as specified in Eq. capture the characteristics in faces and
(1) and in equation Eq. (2): fingerprints. Experimental results on an extensive
1N set of face (FETRET database) and fingerprint
  ()
FRR FRR
N1

n
n
…(1) database (FVC-2004 database) demonstrate that
the proposed correlation and wavelet method
1N outperforms in identification. It is shown that the
  ()
n…(2
FAR FAR )
N1

n proposed method gives satisfying results for
threshold-0.7. There is a trade-off between FAR
82
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4. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 2, April 2012
and FRR values. In some access control system [15] Masatsugu Ichino, Hitoshi Sakano and Naohisa
with more security such as allow to access some Komatsu , ―Multimodal Biometrics of Lip Movements and
Voice using Kernel Fisher Discriminant Analysis ―,ICARCV
important data, it is the false acceptance rate that is 2006 IEEE
major concern that is, we do not want to access the [16] Michał Chora, ―Emerging Methods of Biometrics
data even the risk of manually examining a large Human Identification‖, 2007 IEEE.
number of potential matches identified by the [17] Stelvio Cimato, Marco Gamassi, Vincenzo Piuri,
biometric system. Result shows that FAR is 0, Roberto Sassi and Fabio Scotti, ―Privacy-aware Biometrics:
Design and Implementation of a Multimodal Verification
which can be applied in such applications. This System‖, 2008 Annual Computer Security Applications
work can be extended to feature level fusion to Conference, 2008 IEEE pp. 130-139.
improve accuracy and robustness. [18] Jian Yang, David Zhang, Jing-yu Yang, and Ben Niu,
―Globally Maximizing, Locally Minimizing: Unsupervised
References
Discriminant Projection with Applications to Face and Palm
[1] Satyanadh Gundimada and Vijayan K. Asari, “Facial Biometrics‖, IEEE Transactions on Pattern Analysis And
Recognition Using Multisensor Images Based on Localized Machine Intelligence, pp.650-664, 2007.
Kernel Eigen Spaces‖, IEEE Transactions on Image [19] M. K. Shahin, A. M. Badawi, M. E. Rasmy, ―A
Processing, Vol. 18, No. 6, PP. 1314-1325, June 2009 Multimodal Hand Vein, Hand Geometry, And Fingerprint
[2] Arun Ross and Anil K. Jain, “Multimodal Biometrics: An Prototype Design For High Security Biometrics‖, CIBEC'08,
Overview‖, Proc. of 12th European Signal Processing 2008 IEEE.
Conference (EUSIPCO), pp. 1221-1224, September 2004. [20] A. Rattani, D. R. Kisku, M. Bicego, and M. Tistarelli,
[3] Damien Dessimoz, Jonas Richiardi, Christophe ―Feature Level Fusion of Face and Fingerprint Biometrics‖,
Champod, Andrzej Drygajlo, ―Multimodal biometrics for 2007 IEEE.
identity documents―, Forensic Science International 167, pp. [21] Norman Poh, Thirimachos Bourlai and Josef Kittler,
154–159, 2007. ―BioSecure DS2: A Score-level Quality-dependent and
[4] Xiaoguang Lu, Yunhong Wangy, Anil K. Jain, Cost-sensitive Multimodal Biometric Test Bed‖
“Combining Classifiers For Face Recognition‖ [22] Arun Ross, Anil Jain, ―Information fusion in
[5] Mohamed Deriche, ―Trends and Challenges in Mono and biometrics‖, Pattern Recognition Letters 24, pp. 2115–2125,
Multi Biometrics‖, Image Processing Theory, Tools & 2003.
Applications, 2008 IEEE. [23] F. Wang and J. Han, ―Multimodal biometric
[6] Anil K. Jain, Arun Ross, and Sharath Pankanti, authentication based on score level fusion using support
“Biometrics: A Tool for Information Security‖, IEEE vector machine‖, Opto−Electronics Review 17(1), pp. 59–64
Transactions on Information Forensics And Security, Vol. 1, [24] Ajay Kumar, Vivek Kanhangad, David Zhang,
No. 2, pp. 125-143, June 2006. ―Multimodal Biometrics Management Using Adaptive
[7] S.D. Sapkal, S.N. Kakarwal, P.S. Revankar, ―Image Score-Level Combination‖, 2008 IEEE.
classification using neural network‖, Proc. Of International [25] Robert Snelick, Umut Uludag, Alan Mink, Michael
conf. ICSCI, pp. 259-263, 2007. Indovina, and Anil Jain, ―Large-Scale Evaluation of
[8] S.D. Sapkal, S.N. Kakarwal, M.D. Malkauthekar, Multimodal Biometric Authentication Using State-of-the-Art
―Classification of facial images using FFNN‖, Proc. Of Systems‖, IEEE Transactions on Pattern Analysis And
International conf. ICACT, pp.435-438, 2008. Machine Intelligence, Vol. 27, pp. 450-455, March 2005.
[9]S.D. Sapkal, S.N. Kakarwal, ―Image enhancement and [26] Kar-Ann Toh, Xudong Jiang, and Wei-Yun Yau,
Feature Extraction for Fingerprint Images: A Review‖, ―Exploiting Global and Local Decisions for Multimodal
National conf. MIT Aurangabad, pp. 04. Biometrics Verification‖, IEEE Transactions On Signal
[10] Satyanadh Gundimada and Vijayan K. Asari, “Facial Processing, Vol. 52, No. 10, pp. 3059-3072, October 2004.
Recognition Using Multisensor Images Based on Localized [27] Kar-Ann Toh,er, and Wei-Yun Yau, ―Combination of
Kernel Eigen Spaces‖, IEEE Transactions on Image Hyperbolic Functions for Multimodal Biometrics Data
Processing, Vol. 18, No. 6, pp. 1314-1325, June 2009. Fusion‖, IEEE Transactions on Systems, Man, And
[11] Miguel Carrasco, Luis Pizarro and Domingo Mery, Cybernetics—Part B: Cybernetics, Vol. 34, pp. 1196-1209,
―Bimodal Biometric Person Identification System Under April 2004.
Perturbations‖, Springer, pp. 114–127, 2007. [28] Kalyan Veeramachaneni, Lisa Ann Osadciw, and
[12] P. Ejarque J. Hernando, ―Score bi-Gaussian equalisation Pramod K. Varshney, ―An Adaptive Multimodal Biometric
for multimodal person verification‖, IET Signal Process., Management Algorithm‖, IEEE Transactions on Systems,
Vol. 3, Iss. 4, pp. 322–332, 2009. Man, And Cybernetics—Part C: Applications And Reviews,
[13] Stephen J. Elliott, Sarah A. Massie, Mathias J. Sutton, Vol. 35, No. 3, pp. 344-356, August 2005.
―The Perception of Biometric Technology: A Survey‖, pp. [29] Dakshina Ranjan Kisku, Ajita Rattani, Phalguni Gupta,
259-264, 2007 IEEE. Jamuna Kanta Sing, ―Biometric Sensor Image Fusion for
[14] Andrew Teoh, S. A. Samad and A. Hussain, ―Nearest Identity Verification: A Case Study with Wavelet-based
Neighbourhood Classifiers in a Bimodal Biometric Fusion Rules and Graph Matching‖, 2009 IEEE, pp.
Verification System Fusion Decision Scheme‖, Journal of 436-439.
Research and Practice in Information Technology, Vol. 36, [30] Arjun V. Mane, Ramesh R. Manza, Karbhari V.
No. 1, pp. 47-62 February 2004. Kale,‖The Role of Similarity Measures in Face Recognition‖,
83
All Rights Reserved © 2012 IJARCSEE

5. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 2, April 2012
International Journal of Computer Science and Application,
Issue-I, pp. 62-65, 2010.
[31] Davide Maltoni, Dario Maio, Anil K. Jain, Salil
Prabhakar, Handbook of Fingerprint
Recognition(Springer), pp 3.
[32] Neil Yager and Ted Dunstone, ―The Biometric
Menagerie‖, IEEE Transactions On Pattern Analysis And
Machine Intelligence, VOL. 32, NO. 2, FEBRUARY 2010,
220-230.
[33] Website: http://www.bromba.com/faq/biofaqe
84
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