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CHAPTER 2
LITERATURE SURVEY
2.1 FINGERPRINT ENHANCEMENT
Sherlock et al (1994) proposed a new technique which uses
contextual filtering in the Fourier domain and block-wise processing. Choi &
Krishnapuram (1997) proposed a vigorous fuzzy logic based fingerprint
image enhancement for removing impulse noise, smoothing out non-impulse
noise and preserving edges of the image. Three different filters are used based
on weighted or fuzzy least squares method.
Kasaei et al (1997) proposed a new fingerprint image enhancement
procedure based on local dominant ridge directions. The image is
standardized and enhanced using directional filtering, which uses a library of
filters based on Dominant Ridge Directions (DRD). The DRDs are used to
form the block direction images, where the core and the delta points are
effectively enhanced. The proposed algorithm results in an effective
representation of the fingerprint images with appreciable quality. The main
drawback is that the procedure takes more time to enhance the image when
compared to other methods.
Hong et al (1998) proposed a fast fingerprint enhancement
algorithm using Gabor band pass filters which are tuned to the corresponding
ridge frequency and orientation to remove the undesired noise while
preserving true ride-valley structures, where all the operations are done in
spatial domain. This algorithm can adaptively improve the clarity of ridge and
furrow structures of the input images based on the estimated local ridge
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orientation and frequency. The algorithm also identifies unrecoverable
regions (which are harmful for minutiae extraction) in the fingerprint and
removes them from further processing. This method produces good
performance in terms of goodness index value. The main drawback is the
error produced in the orientation estimation block gets propagated to
frequency estimation thus producing imperfect reconstruction.
Natchegael & Kerre (2000) proposed a new methodology for
fingerprint image enhancement based on fuzzy. Greenberg et al (2002)
anticipated the use of an anisotropic filter that adapts its parameters to the
structure of the underlying sub region. In this method, Wiener filter is used
for de-noising and the adaptive anisotropic filter is used for determining the
local ridge orientation. The enhanced anisotropic filter does not use the
diffusion technique and it is robust to noise, thus restoring the true
ridge/valley of the fingerprint image. This method is less efficient for bad
quality images.
Yang et al (2003) modified the method proposed above by discarding
the inaccurate prior assumption of sinusoidal plane wave and making the
parameter selection process independent of fingerprint image.
Wu et al (2004) planned to convolve a finger print image with an anisotropic
filter to remove the Gaussian noise and then apply directional median filter to
remove impulse noise. The fingerprint image is normalized to reduce the
variations of gray-level values along the ridges and valleys and then the
orientation fields are computed based on chain-code. This causes restoration
discontinuities at the block boundaries. The algorithm fails, when image
regions are contaminated heavily with noise and orientation estimation
becomes too hard.
Chikkerur et al (2007) follows a block-wise Short Term Fourier
Transform (STFT) based enhancement followed by contextual filtering using
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raised cosines. This method estimates all the intrinsic properties of the
fingerprint images such as the foreground region mask, local ridge orientation
and local ridge frequency. This method needs more space requirement than
the other Fourier domain methods. The method proposed is probabilistic and
does not suffer from outliers. This methodology utilizes the full contextual
information like orientation, frequency and angular coherence for
enhancement. In addition to this, the method reduces the space requirements
when compared to other methods. The main drawback is that the proposed
approach doe not uses any technique for smoothing the image before
enhancement.
Fronthaler et al (2007) introduced a new method to enhance the quality
of the given fingerprint image for achieving better recognition performance.
This method adopts a Laplacian like image scale pyramid to decompose the
original fingerprint into three smaller images corresponding to three different
frequency bands. Then, contextual filtering is applied using the three pyramid
levels and one dimensional Gaussian, where the filtering directions are
derived from the linear symmetry features.
Kyung & Bae (2008) proposed a novel method for enhancement which
is heavily dependent on the quality of the fingerprint images. The image is
divided into three classes based on the quality features. Then adaptive
enhancement is applied separately for oily image and dry images. This
method has the possibility of introducing more false minutiae points into the
fingerprint image.
Chengpu et al (2008) proposed an enhancement technique based on
the combination of Gabor filters and diffusion filters. In this paper, an
effective and robust algorithm for fingerprint enhancement has been
proposed. Contrast stretching approach is used to improve the clarity between
foreground and background of the fingerprint image. Then the structure tensor
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property is utilized to estimate the fingerprint orientation and thus it improves
the accuracy of orientation estimation. The advantages of Gabor filtering
method and diffusion filtering method are incorporated and a low-pass filter is
used at the direction that is parallel to the ridge and a band-pass filter is
adopted at the direction that is perpendicular to the ridge. The results show
that the proposed algorithm performs better within less time
Fronthaler et al (2008) proposed a method for enhancement which
is based on the linear symmetry features of fingerprint image. In this method,
the enhancement is applied progressively in the spatial domain. Both absolute
frequency and orientation of the fingerprint image are used for enhancing the
image. All the needed image processing operations are done in spatial domain
thus avoiding block artifacts which reduces the biometric information. In
addition to this, the parabolic symmetry property is used for extracting the
minutiae points from the fingerprint images.
Bansal et al (2009) proposed fingerprint enhancement techniques
by reducing impulse noise from digital images using type-2 fuzzy logic filters.
Karimimehr et al (2010) proposed a novel wavelet based approach for image
enhancement which uses both Gabor wavelet and Gobor filter for the
enhancement purpose. Sixty four Gabor wavelets based on sixteen directions
and four directions are designed to find the local orientation and frequency of
the region in 16 x 16 block. Since this method performs frequency and
orientation estimations independently and simultaneously, the error from each
stage does not influence the other stages. This method does not improve the
blur regions of the fingerprint image effectively.
Bahaghighat et al (2010) developed a new fingerprint enhancement
algorithm by extracting simultaneously the frequency and orientation of the
local ridge in the fingerprint image using Gabor wavelet filter bank. This
robust fingerprint image enhancement procedure is based on the integration of
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Gabor filters and directional median filters. Gabor filters are used to reduce
the Gaussian noises and impulse noises are reduced by directional median
filters. The proposed procedure significantly improves the accuracy rate
when compared to the existing methods with high time complexity.
Cheunhorng & Lin (2010) proposed fingerprint enhancement based
on adaptive color histogram and texture features. Ryu et al (2011) presented a
novel technique for enhancing low quality fingerprint images using Stochastic
Resonance (SR). Stochastic resonance refers to the process of adding Gaussian
noise to low quality fingerprint images to improve the signal-to-noise-ratio
thereby increasing the enhancement.
Stephen & Reddy (2012) proposed a new methodology for fingerprint
image enhancement with ridge orientation using neural network followed by
ternarization. This method utilizes back propagation network with eleven
input nodes, eleven hidden nodes and one output node for learning. This is
followed by ridge orientation estimation using the response obtained during
the learning process. The use of neural network has reduced the rate of false
minutiae extraction to a great extent. The quality of the enhancement image is
not satisfactory.
Babatunde et al (2012) proposed a modified version of a mathematical
algorithm for improving the quality of the fingerprint image enhancement.
The modified algorithm consists of sub-models for fingerprint segmentation,
normalization, ridge orientation estimation, Gabor filtering, binarization and
thinning. The proposed method performs well for synthetic and real
fingerprint images with free or minimum noise level.
Raajan & Pannirselvam (2012) proposed an efficient methodology for
fingerprint image enhancement using high boost Gaussian filter. In this
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method, the original fingerprint image is first high pass filtered and then
Gaussian filter is applied to remove noise. This step is followed by the
application of a high boost filter to achieve better performance.
Selmani et al (2013) proposed a robust filtering method based on fuzzy
logic was proposed. The main feature of the proposed filter is that it tries to
determine the best filter for all the noise intensities. The filter is able to
perform a very strong noise cancellation compared with static median filter.
Bartunek et al (2013) proposed an adaptive fingerprint enhancement
technique based on contextual filtering. The method involves preprocessing of
data on global and local level using the non-linear successive mean
quantization transform dynamic range adjustment method to enhance the
global contrast of the fingerprint image. The proposed method combines and
updates the existing processing blocks in to a new and robust fingerprint
enhancement system which leads to a drastically increased performance
where the equal error rate and the area above the curve is increased. The
proposed algorithm is insensitive to the various characteristics of the
fingerprint images obtained by different sensors.
From all the above discussed survey of fingerprint enhancement
techniques, it is clear that the enhancement techniques, either spatial or
frequency domain could not meet the needs for any real time AFIS in
improving the valley clarity and ridge flow continuity. The performance of
enhancement techniques relies heavily on local ridge orientation of the
fingerprint image. By considering the inefficiency of the existing techniques,
there is a need to propose a new enhancement methodology which focuses on
ridge orientation to improve the quality of the fingerprint image.
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2.2 FINGERPRINT MINUTIAE EXTRACTION AND FALSE
MINUTIAE REMOVAL
Leung et al (1991) proposed a neural network based approach for
the extraction of minutiae where the preprocessing technique is first applied
to a clean and thinned binary fingerprint ridge structure. A multilayer
perceptron network with three layers is trained to extract minutiae from
thinned binary image. The original fingerprint image is first convolved with
complex Gabor filter and the resulting magnitude and angle are passed as
inputs to a back propagation neural network to identify minutiae points. This
method produces good detection ratio and also it leads to low false alarm rate.
Ratha et al (1995) introduced a new algorithm in which the flow
direction is computed by viewing the fingerprint image as a directional
textured image. This method used orientation flow field to design adaptive
filters that are applied on the input fingerprint images. A waveform projection
based ridge segmentation algorithm is used to detect ridges. Morphological
operations are used for smoothing the ridge skeleton image. The spurious
minutiae points are removed using some heuristics and the problem with these
heuristics is that they do not eliminate all possible defects in the input gray
level fingerprint image. This method produces reasonable goodness index
value when compared with other methods.
Xiao & Rafaat (1995) proposed a false minutiae removal algorithm
based on both statistical and structural information. This method heavily
depends on connectivity which makes it complex and unreliable to bad
quality fingerprints. Jain et al (1997) proposed a minutiae extraction
algorithm that directly extracts minutiae from gray level images based on
distance and connectivity criteria. This method eliminates the true minutiae
points and also eliminates false minutiae points.
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Maio & Maltoni (1998) proposed a minutiae extraction algorithm
that directly extracts minutiae from gray level images by following ridge lines
with the help of the local orientation field. This method is based on a ridge
line following algorithm that follows the image ridge lines until a ridge
ending or a bifurcation occurs. This method is superior in terms of efficiency
and robustness when compared to other methods but is has high
computational complexity.
Sagar & Beng (1999) proposed a fuzzy rule method based on
human linguistics for minutiae extraction for gray level images.
Farina et al (1999) proposed a novel method for extracting minutiae points
from skeletonized and binarized images. This method proposes a new method
for bridge cleaning based on ridge position instead of directional maps. New
algorithms are proposed for ridge point validation and bifurcation validation
which are more reliable and can be used in different applications. The
proposed method reduces the spurious minutiae points considerably with high
computational power.
Chikkerur et al (2004) proposed a new method for extracting global
and local fingerprint features based on Fourier analysis. A chain coded
contour following method is proposed which uses lossless representation of
contours for effective minutiae point detection. This paper adopts the usage of
heuristic rules based on structural properties of the minutiae points for
eliminating false fingerprint features. The algorithm detects the true minutiae
points effectively but the chain code representation increases the complexity.
Hwang et al (2005) proposed a fast method for minutiae extraction
based on horizontal and vertical run length encoding from un-thinned binary
images without using a computationally expensive thinning process.
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Gamassi et al (2005) proposed a new square based method to
identify minutiae points in the fingerprint images based on the analysis of
local properties. Ridge endings and bifurcations are identified by studying the
intensity along the squared path of the un-thinned binarized image. This
method achieves remarkable identification accuracy. The major drawback is
that the method is not fully adaptive with respect to all parameters.
Fronthaler et al (2005) proposed a new method for local feature
extraction in fingerprint images using complex filtering technique. This paper
proposes a pair of local feature descriptors namely linear symmetry and
parabolic symmetry features for fingerprint feature extraction. Minutiae
points are detected by means of complex filters which reveals not only on the
feature position but also on directions of the feature points. The proposed
methodology is fast and efficient in extracting minutiae points, but it does not
consider either the global features or the size of the available fingerprint area.
Shi & Govindaraju (2006) introduced chain code processing
method for minutiae extraction which is extensively used in document
analysis and they are mainly meant for un-thinned binarized images. The
chain code representation allows efficient image quality enhancement and
detection of fine minutiae points from fingerprint images. The enhanced
fingerprint image is subjected to binarization using a locally adaptive
binarization method. The minutiae points are detected using a more
sophisticated ridge contour following procedure. A new post processing stage
for removing spurious minutiae points is also added. The chain code method
is efficient in extracting minutiae points but the post processing stage do not
remove added minutiae and exchanged minutiae due to noise from sweat
pores in ridges.
Humbe et al (2007) introduced a new method for removing
unnecessary information for true minutiae extraction technique based on
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mathematical morphology. This morphological operation based algorithm
removes spurs, spikes and dots effectively and also it clearly extracts a map
structure from the input fingerprint image. This algorithm produces better
accuracy rate but some of the true minutiae points are missed.
Kaur et al (2008) developed an enhanced thinning algorithm for
effective minutiae extraction by eliminating erroneous pixels and by
preserving the connectivity property of each pixel. This paper uses distance
criteria for false minutiae elimination. The enhanced thinning algorithm
improves the complexity of thinning process.
Kim et al (2009) proposes a new robust minutiae post-processing
algorithm which uses orientation and flow of ridges for detecting minutiae
points. False minutiae removal is achieved by using simple decision rules
which are framed on distance, connectivity, orientation and flow of ridges.
This method improves the fingerprint matching performance by effectively
eliminating false minutiae points while retaining true minutiae points. The
main drawback is that this method produces incorrect acceptance and false
rejection rate for bad quality fingerprint images.
Alibeigi et al (2009) proposed a hardware scheme based on
pipelined architecture for minutiae extraction and false minutiae elimination.
The proposed method extracts the fingerprint minutiae from binary image
effectively but with high computational complexity.
Bansal et al (2010) proposed a new algorithm for extracting
minutiae form fingerprint image using the binary hit and miss transform of
mathematical morphology. This method uses a pre-processing stage which
involves morphological operators to remove superfluous information
followed by thinning. Minutiae extraction using hit and miss transform
reduces the effort of post processing stage since more number of false
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minutiae points are removed comparatively while detecting the points itself.
Simple distance based false minutiae removal method is adopted in this paper.
Gnanasivam & Muttan (2010) proposed an efficient algorithm for
fingerprint feature extraction based on vertical orientation of ridges and
connected component analysis concept. The minutiae extraction process is
accomplished by block processing which involves a line based feature
extraction algorithm, connected components and ridge tracing approach. The
proposed minutiae extraction algorithm improves the performance of
matching fingerprints with some limitations in vertical orientation process and
also the proposed method has high computation time and high cost.
Gao et al (2010) proposed a new minutiae extraction method which
is based on Gabor phase. This method works in the transform domain of
fingerprint image where the image is convolved by a Gabor filter which
results in a complex image. The complex image is then transformed into its
corresponding amplitude and phase part. Finally a minutiae extractor extracts
the minutiae points directly from the Gabor phase field.
Stephen et al (2012) developed a new idea for extracting minutiae
points and removal of false minutiae points by implementing some simple
fuzzy rules based on the distance criteria. The false minutiae points are then
exported to a text file in the workspace. Stephen et al (2013) proposed a post
processing technique for the removal of false minutiae points from the
fingerprint image. A new fuzzy rule based false minutiae elimination system
is proposed with modified if-then rules by considering the issue of not
removing the true minutiae points in post processing. The paper achieves the
aim effectively with respect to easy implementation and high computational
effort.
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From all the above discussed minutiae extraction techniques, it is
observed that most of the extraction technologies and false minutiae
elimination methodologies suffer from problems associated with the handling
of poor quality impressions, distortions in both geometric position and
orientation and difficulties in getting a match among multiple impressions of
the same fingerprint. To overcome the above said limitations, an efficient post
processing stage is necessary to achieve good false accept rate and false reject
rate in fingerprint matching with less computational efficiency.
2.3 FINGERPRINT RECOGNITION
The problem of automatic fingerprint recognition has attracted wide
attention among researchers worldwide and has led to extensive research in
this area. Grasselli (1969) was the first to find the linguistic approach for
fingerprint classification. Jain et al (2001) proposed a hybrid matching
algorithm that uses both minutiae information and texture information for
matching the fingerprints. The computational requirement of the hybrid
matcher is dictated by the convolution operation associated with the Gabor
filters. The proposed method substantially improves the system performance
but the speed of the algorithm is very low when compared to other methods.
Willis & Myers (2001) developed a robust algorithm which allows
good recognition of low quality fingerprints by simultaneously smoothing and
enhancing poor quality fingerprints derived from a database of imperfect
fingerprints. A number of neural network based classifiers are analyzed to
select an optimal classifier. Correlation based matching technique with slight
improvement was adopted to cross correlate the wavelet transform of two
fingerprints. The proposed method works well for good fingerprints. The
method is computationally inexpensive as long as the resolution of the
fingerprint image is kept low.
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Abdallah (2005) proposes a new fingerprint identification
technique based on ANN, in which a novel clustering algorithm is used to
detect similar feature groups from multiple template images, thus forming
cluster set. In the proposed method, quick response is achieved by
manipulating the search order inside the experimental database. The
configuration of the artificial neural network system used in this approach
provides good generalization ability and sufficient discrimination capability.
The proposed approach provides efficient one-to-many matching of
fingerprints on large databases.
Cappelli et al (2006) evaluates the performance of fingerprint
verification systems based on the theoretical and practical issues related to the
performance evaluation of Fingerprint Verification Competition (FVC 2004).
The paper introduced a simple and effective method for comparing algorithms
at the score level and studies error correlation and algorithm fusion. This
paper provides huge information about the verification systems which are
useful for research.
Barreto et al (2006) provides method for fingerprint image
enhancement using neural networks. A computational segmentation method is
applied to detect the region of interest on fingerprint images. The approach is
based on the hypothesis that a small fingerprint fragment resembles a two-
dimension sinusoid function. Therefore, its Fourier spectrum must present a
well-defined pattern. Since neural networks are very suitable for solving
pattern recognition problems, a multi layer perceptron network is used to
discriminate the regions containing fingerprint fragments from the rest of the
image. The proposed model is tested over fingerprint images obtained from
the NIST special database 27, and the obtained results demonstrate that the
approach works reasonably well for images with different noise and contrast
levels.
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Rashid & Hossain (2006) proposed a fingerprint recognition
scheme in which certain features of fingerprint image are applied to the back
propagation network for training purpose and the values of the nodes are
updated and stored in a relational knowledge base. For fingerprint
recognition, the verification part of the system identifies the fingerprint of a
person with the help of the previous experimental values which are store in
relational database. The accuracy produced by the proposed method is not
satisfactory when compared to other existing methods, because only the
position of the minutiae points are considered (orientation of the ridge, core
and delta points are not considered).
Gu et al (2006) proposed a novel representation of fingerprint
which includes both minutiae features and model based orientation field.
Fingerprint matching is done by combining the decision of the matcher based
on global structure (orientation) and local cue (minutiae). This ensemble
classifier considerably improves the performance of the system. The system is
more robust but it takes more time for completion.
Arivazhagan et al (2007) introduced a new approach for fingerprint
verification based on Gabor co-occurrence features of the fingerprint image.
The proposed Gabor wavelet transform based method provides both local and
global information of the fingerprint in fixed length finger code. Then
fingerprint matching is done by means of finding the Euclidean distance
between the two corresponding finger codes. The recognition rate of the
proposed method is not satisfactory for non-overlapping images.
Ravi et al (2009) proposes fingerprint recognition using minutiae
score matching method, in which the extracted minutiae are stored in matrix
form and matching is done using matching score. . During matching process,
each minutiae point is compared with the template minutiae point. The
outputs produced are the reference points which are used to convert the
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remaining data points to polar coordinates. The proposed method is not
effective for low quality images.
Ashwini & Mukesh (2010) has proposed an effective fingerprint
matching algorithm based on feature extraction. A novel fingerprint
recognition technique for minutiae extraction and a minutiae matching has
also been introduced which is base on alignment based method.
Thai & Tam (2010) proposed a new fingerprint recognition system
using standardized fingerprint model which is used to synthesize the
templates of fingerprints. In this model, after pre-processing step,
transformation between templates, parameter adjustment and fingerprint
synthesization are done for achieving effective fingerprint matching. The
accuracy rate is stumpy when compared to other existing methods.
Chandrabhan et al (2010) has combined many methods to build a
minutiae extractor and a minutiae matcher. This method adopts alignment
based matching algorithm consisting of two stages. The first phase is called as
the alignment stage where the minutiae points of two fingerprints are aligned
based on a similarity measure. This stage is followed by the match phase,
where an elastic match algorithm is used to count the matched minutiae pairs.
Fingerprint alignment and matching stage improves the matching score but
the system is not generally reliable.
Kekre & Bharadi (2010) has used correlation based fingerprint
recognition based on multiple features derived from the fingerprint and are
collectively used for consistent core point detection. This method integrates
the sine component of the orientation field with three segments and is linearly
summed to produce a good approximation of the fingerprint with more
number of iterations. Then core point is estimated using poincare index. The
overall accuracy rate for unconstrained database is low.
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Lourde & Khosla (2010) discussed the issue of selection of an
optimal algorithm for fingerprint matching in order to design a system that
matches required specifications in performance and accuracy. This paper also
says that in order to achieve desired accuracy and system performance, it is
necessary to completely understand the specifications and implementation
details of the existing methods.
Pornpanomchai & Phaisitkulwiwat (2010) adopted the traditional
method of fingerprint recognition based on Euclidean distance method. The
recognition process consists of three steps. The first step deals with the
calculation of Euclidean distance between the core point and the bifurcation
point in the sixteen sectors. Second step compares all the sixteen Euclidean
distance between the training and testing data sets. Finally, best match is
selected. This method improves the system performance but the average
access time per image is high when compared to other systems.
Sengar et al (2012) designed a supervised neural network for
fingerprint images using the three basic patterns whorls, arches and loops.
The output produced by this method improves the recognition rate to a certain
extent but the complexity in implementing the proposed algorithm is high.
Mirzaei et al (2013) proposed a new recognition approach based on
the number, location and surrounded area of the singular points. The classifier
is rule based, where the rules are generated independent of a given set. The
proposed method is invariant to translation, rotation and scale changes. The
accuracy of this method is improved significantly. The main drawback with
this method is that some of the images are misclassified because of inefficient
rules.
Zhou et al (2013) introduced a new recognition technique based on
scale invariant feature transformation descriptors. These descriptors are
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employed to fulfill the verification tasks associated with low quality
fingerprints with lot of cuts or scratches. A two-step matcher called as
improved all descriptor pair matching is also proposed to implement the 1:N
verifications in real time. The proposed fingerprint identification scheme
achieves a significant improvement in accuracy when compared with the
conventional minutiae based methods with high complexity.
Goranin et al (2013) introduced the concept of using genetic algorithm in
optimizing the choice of positioning the fingerprint.
From all the aforementioned fingerprint recognition techniques, it is
noted that most of the recognition techniques has its own weaknesses like
poor recognition because of complex distortions in images, creation and usage
of fingerprint test databases and high time complexity for recognizing low
quality fingerprint images. To provide better solutions for the above said
limitations, a well-organized recognition system is obligatory to deal with the
low quality fingerprint images in an optimal way.
2.4 FINGERPRINT SECURITY DURING TRANSMISSION
2.4.1 Data Hiding
Weinberger & Sapiro (2000) introduced the JPEG-LS predictor
which aims in reducing the difference value. For eliminating replay attack,
that is, where a previously intercepted biometric is replayed, Ratha et al
(2001) proposed a challenge/response based system. A pseudo-random
challenge is presented to the sensor by a secure transaction server. The sensor
acquires the current biometric signal and computes the response for that
challenge. Then, the acquired signal and the response computed are compared
against the received signal in the transaction server for consistency. An
inconsistency reveals the possibility of resubmission attack. A novel method
is also developed to protect templates from false usage which involves an
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imprecise and non-invertible version of the biometric signal or the feature
vector. In addition to this, the fingerprint is secured by hiding messages by the
use of authentication stamps such as personal identification information in the
compressed domain.
Tian (2003) introduced a difference expansion based reversible
watermarking technique which creates space by expanding a difference. The
data and the secondary information are further added to the expanded
difference and are embedded into the image. In this method the differences
between adjacent pixels are doubled to generate a new Least Significant Bit
(LSB) plane for accommodating additional data.
Alattar (2004) proposed an attack on a face recognition system
where a specific user is attacked through synthetically generated images. At
each step, several images are multiplied with a weight and added to the
current candidate image. The modified image is given as input to the new
candidate image. These iterations are repeated until no improvement in
matching score is obtained, which is calculated as a sigmoidal function.
Kundar & Karthik (2004) proposed a method for watermarking in
which the content owner encrypts the signs of host DCT coefficients and each
content user uses a different key to decrypt a subset of the coefficients, so that
a series of different fingerprint versions are generated. Wu & Memon (2004)
proposed the Gradient Adjusted Predictor (GAP) which is used in context
based adaptive lossless image coding algorithm to provide better results.
Kuribayashini & Tanaka (2005) proposed a method in which each sample of a
cover signal is encrypted by a public key mechanism and a homomorphic
property of encryption is used to embed some additional data in to the
encrypted signal. Kamstra & Heijmans (2005) and Coltuc & Chassery (2007)
calculated the expanded difference by taking the difference between the
adjacent pixels. Ni et al (2006) projected a method in which a data hider can
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also perform reversible data hiding using a histogram shift mechanism, which
utilizes zero and peak points of the histogram and slightly modifies the pixel
gray values to embed data into the image.
Thodi & Rodriguez (2007) and Sachnev et al (2009) introduced a
method in which the differences between the predicted pixels are taken into
account. Lian et al (2007) proposed joint data hiding and encryption scheme
in which a part of cover data is used to carry the additional message and the
rest of the data is encrypted. In this method, the vector difference and the
DCT coefficients are encrypted, while a watermark is embedded into the
amplitudes of the DCT coefficients. Soutar proposed a hill climbing attack for
a simple image recognition system which is based on filter based correlation.
Synthetic templates are gradually subjected as input to a biometric
authentication system. Soutar also showed that the system could be
compromised till the point of incorrect positive identification.
Lee et al (2008) developed a method for watermarking by
considering the pixels of a block and the mean value of the block. To
minimize the difference value the watermarking techniques are built on high
performance predictors. Luo et al (2010) introduces reversible watermarking
scheme through interpolation technique. Hong et al (2010) uses orthogonal
projection and prediction error based modification for watermarking.
Cancellaro et al (2010) introduced a method in which the cover data in higher
and lower bit planes of transform domain are respectively encrypted and
watermarked.
Coltuc (2011) proposed a modified data embedding procedure for
prediction error expansion reversible watermarking scheme in which the
prediction error is not only embedded into the current pixel but also into its
prediction context. Zhang (2011) proposed a lossy compression and iterative
reconstruction for encrypted images. A novel reversible data hiding scheme is
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also proposed in which the data of the original cover is entirely encrypted and
the additional message is embedded by modifying a part of the encrypted
data. Coltuc (2012) proposed a low distortion transform based digital
watermarking scheme for secured transmission of data in which the classical
prediction error expansion is split uniformly into four parts thus reducing the
distortion when compared to methods based on high performance predictors.
From the above mentioned survey, it is found that, low difference
values ensure low distortion of embedding. In order to minimize such
differences, most of the watermarking schemes are built on high performance
predictors, which increase the mathematical complexity. The scheme
proposed by Coltuc (2012) reduces the distortion introduced by the
watermarking by considering a simple predictor (JPEG4) together with an
optimized data embedding procedure.
2.4.2 Data Integrity
Authentication, verification and identification system helps in
identifying a person. Accurate automatic personal identification is becoming
more and more important to the operation of the increasingly interconnected
information society. Conventional automatic personal identification
technologies such as password, tokens, identification card etc., verify the
identity of a person and are no longer reliable to satisfy the security
requirements of electronic transactions. All the traditional techniques suffer
from a common problem of their inability to differentiate between an
authorized person and an impostor who illegally acquires the access privilege
of the authorized person. In addition to this, if they are not properly
implemented, then it leads to misevaluation of the results.
In network security, data integrity is the assurance that, the data
received by the receiver is exactly the same as that of the authorized sender
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without any modification, insertion, deletion or replay. Thus integrity refers to
the validity of data. Message authentication is a mechanism that is used to
verify the integrity of a message. It ensures that the data received are exactly
as sent by the sender and that the purported identity of the sender is valid.
Symmetric encryption provides authentication among those who share the
of authentication.
Biometric is a technology that uniquely identifies a person based on
something you are and therefore they can naturally differentiate an authorized
person from an un-authorized one. However, fingerprint based authentication
workstation, which is potentially a pathetic point in the security system.
Though the communication channel is encrypted, the stored fingerprint
images might be fraudulently transmitted. To enhance this, additional
information is directly embedded in fingerprint images using appropriate data
hiding techniques.
The two most common cryptographic techniques for message
authentication are Message Authentication Code (MAC) and hash code. MAC
is an algorithm which takes a variable length message and a secret key as
input and produces an authentication code. This code is compared with the
code generated by the recipient to verify the integrity of the message. A hash
function maps a variable length message into a fixed length hash value or
message digest, which serves as the authenticator. The hash algorithms MD5
and SHA-1 are inbuilt with compression function to avoid collision and
generates message digest without the key. MD5 algorithm (RFC1321) was
developed by Ron Rivest in the year 1990 that converts variable size input to
128 bit message digest (Stallings 2006). Secure Hash Algorithm (SHA-1) was
developed by the National Institute of Standards and Technology (NIST) and
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published as a Federal Information Processing Standard (FIPS 180) in 1993; a
revised version was issued as FIPS 180-1 in 1995 and is generally referred to
as SHA-1 (Stallings 2006). SHA-1 converts variable size input to a 160 bit
fixed size output.
2.5 NEURAL NETWORKS
Neural Networks (NN) are simplified models of the biological
nervous system. A neural network is a highly interrelated network consisting
of a large number of processing elements called as neurons and they are
inspired by the brain. Neural network is learned by examples. Neural
networks exhibit mapping capabilities in the sense that they can map input
patterns into its associated output patterns. Neural network is trained with
known examples of a problem to acquire knowledge about it. They possess
the capability to generalize, that is, they can predict new outcomes from past
trends. They are usually robust systems and are fault tolerant. They process
information in parallel at high speed in a distributed manner. Neural networks
adopt various learning mechanisms of which supervised learning and
unsupervised methods have turned out to be very popular. In supervised
learning, teacher is assumed to be present during the learning process. The
network aims to minimize the error between the target (desired) output
presented by the teacher and the computed output to achieve better
performance (Jang & Sun 1997).
Neural network architectures have been broadly classified as single
layer feed forward networks, multilayer feed forward networks, and recurrent
networks. Some of the familiar neural network systems include Back
Propagation Network (BPN), Perceptron, ADALINE (Adaptive Linear
Element), Associative Memory, Boltzmann Machine, Adaptive Resonance
Theory, Self-Organizing Map, and Hopfield Network (Fausett 2004). Neural
networks have been beneficially applied to the problems in the fields of
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pattern recognition, image processing, data compression, forecasting, and
optimization. Neural algorithms are applied in feature extraction of zero-
watermark scheme for digital images (Sang et al 2006). Neural algorithms are
also used in steganalysis of still images. The embedded information is fed to
the neural networks to get secret message. This method is used for internet
security, watermarking, etc. The new release V3.0 of NEUROGRAPH is the
first soft-computing simulation environment combining neural networks, fuzzy
logic and genetic algorithms.
2.6 GENETIC ALGORITHM
Decision making features arise in all fields of human activities such
as scientific and technological applications and can affect every globe of our
life. Modeling biological evolution was done even in the formative years of
urbanized evolutionary algorithms for optimization and machine learning. In
1965, Rechenberg introduced evolution strategies, a method used to optimize
real valued parameters for devices. Genetic algorithms were invented by
Holland in the year 1960 and were later developed by Holland, his students
and colleagues at the University of Michigan in the year 1970 (Goldberg &
Deb 2008). Adaptation in Natural and Artificial Systems
presented the genetic algorithms as a generalization of biological evolution
and gave a hypothetical framework for adaptation under the genetic
algorithms. Genetic algorithms are computerized search and optimization
algorithms based on the technicalities of natural genetics and natural selection
(Davis 1991).
-world applications took
place due
pattern recognition, flow control devices, structural optimization, micro-chip
design, aerospace applications and micro-biology. Genetic algorithm is
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defined as a probabilistic search algorithm that iteratively transforms a set
(called a population) of mathematical objects (typically fixed-length binary
character strings), each with an associated fitness value, into a new population
selection and
using operations that are patterned after naturally occurring genetic
operations, such as crossover (sexual recombination) and mutation
(Rajasekaran & Pai 2003).
Till date, most of the GA studies are accessible through some books
by Davis (1991), Golberg (1989), Holland (1975) and Deb (1995). The first
application towards structural engineering was carried by Goldberg &
Samtani (1986). They applied genetic algorithm to the optimization of a ten-
member plane truss. Jenkins applies genetic algorithm to a trussed beam
structure. Deb (1999) applied GA to structural engineering problems. Apart
from structural engineering, it can also be used in biology, computer science,
image processing, pattern recognition and neural networks.
A hybrid methodology for combining genetic algorithms and search
algorithms has time-honored considerable attention. Nowadays hybrid genetic
algorithms are used to find optimal solutions in large search space. To find the
optimal solution in large search space corresponding to a given problem, the
better individuals have to be copied to the next generation for improving
convergence and finding the best individual to increase the efficiency and to
increase the efficacy of finding the best individual.
2.7 FUZZY LOGIC
Uncertain information can take on many different forms. Uncertainty
arises because of complexity, unawareness, arbitrariness, inadequate
measurements and lack of knowledge. Fuzzy sets afford an arithmetical way
to represent vagueness and fuzziness in humanistic systems. Fuzzy logic is a
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form of many-valued logic which deals with reasoning that is approximate
rather than fixed and exact. Compared to traditional binary sets (where
variables may take on true or false values) fuzzy logic variables may have a
truth value that ranges in degree between 0 and 1. When linguistic variables
are used, these degrees may be managed by specific functions. The term
"fuzzy logic" was introduced in the year 1965 by Zadeh. Fuzzy logic has been
applied to many fields, from control theory to artificial intelligence. All the
physical processes are based largely on imprecise human reasoning. This
imprecision is a form of information that can be quite useful to humans. The
ability to embed such reasoning in determined and complex problems is the
decisive factor by which the efficacy of fuzzy logic is judged. From the
historical point of view, the subject of uncertainty has not always been
embraced within the scientific community (Klir et al 1988). In the traditional
view of science, uncertainty represents an undesirable state that must be
avoided at all costs. The leading theory in quantifying uncertainty in scientific
models from the late nineteenth century until the late twentieth century has
been the probability theory. Black (1973) expresses uncertainty using
probability theory. Zadeh (1965) introduced his influential idea in a
continuous valued logic called as fuzzy set theory. In the year 1980, other
investigators showed a tough relationship between evidence theory,
probability theory and possibility theory.
In 1973, with the basic theory of Zadeh fuzzy controllers, other
researchers began to relate fuzzy logic to various mechanical and industrial
processes. Professors Terano and Shibata in Tokyo, along with Professors
Tanaka and Asai in Osaka, made major contributions both to the development
of fuzzy logic theory and its applications. In 1980 Professor Mandani in the
United Kingdom, designed the first fuzzy controller for a steam engine with
great success. In 1987 Hitachi used a fuzzy controller for the Sendai train
control. It was also during this year of 1987 when the company Omron
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developed the first commercial fuzzy controllers. So the year 1987 is
roducts based on
fuzzy logic to be traded. In 1993, Fuji applied fuzzy logic to control chemical
injection water treatment plants for the first time in Japan. In addition to the
study of the applications of fuzzy logic, Professors Takagi and Sugeno
developed the first approach to construct fuzzy rules. The fuzzy rules, or rules
of a fuzzy system, define a set of overlapping patches that narrate a full range
of inputs to a full range of outputs. In that sense, the fuzzy system
approximates some mathematical function or equation of cause and effect.
Recent advances in neural networks and genetic algorithms are certainly a
fitting complement to fuzzy logic. Neuro-fuzzy systems uses learning
methods based on neural networks to identify and optimize.