Palm Print Authentication

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PALMPRINT AUTHENTICATION
SYSTEM

GROUP NUMBER : 16
DEPARTMENT : INFORMATION TECHNOLOGY
YEAR : 3RD
SEMESTER : 6TH

GROUP MEMBERS :
ARNAB ROY (48)
ISAN KUMAR BOTHRA (10)
AWANISH KUMAR (34)

INTRODUCTION :
The widespread penetration of information technology into our daily lives has
triggered the real need for reliable and user friendly mechanism to authenticate
individuals.

Personal authentication using palmprint has emerged as a promising component of
biometric study. While palmprint based authentication approaches have shown
promising results, has emerged as one of the most popular and promising biometric
modalities for personal identity verification due to its ease of acquisition, noninvasive procedure, high user acceptance and reliability. Efforts are still required to
achieve higher performance for their use in high security applications. Prior work on
palmprint authentication has shown promising results on inked, scanned, and
constrained images, there is great need for better performance in images acquired
from unconstrained peg-free setup.

Palmprints have several advantages over other hand-based biometrics, such as
palmprint and hand geometry. Compared to palmtips, palms are larger in size and
therefore are more robust to injuries and dirt. Also, low-resolution imaging can be
employed in the palmprint recognition based on creases and palm lines, making it
possible to perform real time preprocessing and feature extraction; and the cost of
the capturing device can also be well controlled. Palmprint authentication is believed
to be able to achieve the accuracy comparable to that of other hand-based biometric
authentication technologies, including palmprint and hand geometry.

One of the possible approaches to achieve higher performance is to integrate
palmprint with other biometrics (multimodal systems) or combine various classifiers
(intramodal systems) that have shown promising results in palmprint authentication.

In the context of recent work on intramodal biometric systems, palmprint also
deserves careful evaluation. Earlier studies have revealed that the palmprint
contains mainly three types of information, i.e., texture information, line information,
and appearance based information. A generic online palmprint based authentication
system considers only texture information while ignoring line- and appearance-based
information. Thus the use of single palmprint representation has become the
bottleneck in producing high performance. However an ideal palmprint based
personal authentication system should be able to reliably discriminate individuals
using all of the available information.

Automated personal authentication using biometric features has been widely studied
during the last two decades. Previous research efforts have made it possible to apply
biometric systems to practical applications for security or commercial purposes.
Biometric systems based on palmprint recognition, face recognition, and iris
recognition have already been developed to a quite mature stage so that they can
be applied to critical security applications such as the immigration control and the
crime investigation.

Recently, a novel hand-based biometric feature, palmprint, has attracted an
increasing amount of attention. Like any other biometric identifiers, palmprints are
believed to have the critical properties of universality, uniqueness, permanence and
collectability for personal authentication. Texture and palm lines are the most clearly
observable palmprint features in low resolution (such as 100 dpi) images, and thus
have attracted most research efforts.

In texture based palmprint authentication approaches, signal processing based
texture analysis methods are usually adopted. Typically, texture features are
extracted by filtering the palmprint images using filters such as the Gabor filter, the
ordinal filter, or the wavelet. The image filtering may be performed in either the
spatial domain or the frequency domain. Recently, a lot of automated palmprint
authentication methods have focused on the palm line features, since they are more
appealing than the texture for the human vision. In the offline method proposed in,
the geometric shapes of the palm lines are extracted and approximated by straightline segments.

The slope, intercept and inclination of each segment are used as features for
palmprint matching. C.C. Han et al. investigate the magnitude of palm lines in
palmprint matching. The latest related research reveals that the orientations of palm
lines also contain strong discriminative power. Based on palm line orientations, a
Competitive Code is designed for palmprint representation in; and Y. Han et al. use
local orientation histograms for describing palmprints. Similar to the texture based
methods; the palm line based methods usually employ image filtering for line feature
extraction, leading to a high computational complexity.

For example, in the Competitive Code method, six Gabor filters are applied to each
palmprint ROI (Region Of Interest) for generating the corresponding orientation map.
Suppose that the Palmprint ROI is 128 x 128 pixels and the Gabor filters are 35 35 in
size, the overall MADD (Multiplication + Addition) operations required for one
palmprint is around 120 million, leading to a very long processing time especially on
slow mobile platforms. Experiments show that extracting the Competitive Code for
one palmprint takes more than eight seconds on a state of the art PDA. This is far too
slow for a real-time biometric system.

Besides the computational complexity, selecting appropriate filter parameters is also
nontrivial in filtering based palmprint authentication methods. It has been
demonstrated in that the authentication accuracy varies a lot when using different
Gabor filter parameters, which need to be tuned in a try and error manner, indicating
that the authentication performance will depend a lot on the training set used for
parameter selection.

This may account for the significant performance variations of different filtering
based palmprint authentication methods on different databases. In this paper, we
propose a texture based approach for palmprint authentication, in which palmprint
image grayscale information are directly adopted as features.

The computational complexity of the feature extraction process is much lower than
previous filtering based approaches, and thus can be implemented efficiently for
even slow mobile embedded platforms. By extending the idea of SAX (Symbolic
Aggregate approximation) in time series research to 2D images for palmprint
representation and matching, the proposed method can achieve the authentication
performance, in terms of EER (Equal Error Rate), comparable to the state of the art
palmprint authentication methods. The rest of this paper is organized as the follows.
Section 2 explains the 2D extension of SAX for images. Section 3 describes the
feature extraction and matching processes of the proposed approach. Experiments
and results are elaborated in Section 4. The last section is a conclusion of our work.

DETAILS :
The analysis of palmprints for matching purposes generally requires the comparison
of several features of the print pattern. These include patterns, which are aggregate
characteristics of ridges, and minutia points, which are unique features found within
the patterns. It is also necessary to know the structure and properties of
human skin in order to successfully employ some of the imaging technologies.

Patterns :
The three basic patterns of palmprint ridges are the arch, loop, and whorl, quite
similar to that of fingerprints, however the texture obtained via the lower part palm
is and the wrist are a bit complex, as explained later:



Arch: The ridges enter from one side of the palm, rise in the center
forming an arc, and then exit the other side of the palm.



Loop: The ridges enter from one side of a palm, form a curve, and then
exit on that same side.



Whorl: Ridges form circularly around a central point on the palm.

1. Arch Pattern
Full Palm

2. Whorl Pattern

3. Loop Pattern

4.

Scientists have found that family members often share the same general palmprint
patterns, leading to the belief that these patterns are inherited.

Minutia features :
The major minutia features of palmprint ridges are ridge ending, bifurcation, and
short ridge (or dot). The ridge ending is the point at which a ridge terminates.
Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or
dots) are ridges which are significantly shorter than the average ridge length on the
palmprint. Minutiae and patterns are very important in the analysis of palmprints
since no two palms have been shown to be identical.

Palmprint sensors :

A palmprint sensor is an electronic device used to capture a digital image of the
palmprint pattern. The captured image is called a live scan. This live scan is digitally
processed to create a biometric template (a collection of extracted features) which is
stored and used for matching. This is an overview of some of the more commonly
used palmprint sensor technologies.

Optical :
Optical palmprint imaging involves capturing a digital image of the print
using visible light. This type of sensor is, in essence, a specialized digital
camera. The top layer of the sensor, where the palm is placed, is known as the
touch surface. Beneath this layer is a light-emitting phosphor layer which
illuminates the surface of the palm. The light reflected from the palm passes
through the phosphor layer to an array of solid state pixels (a charge-coupled
device) which captures a visual image of the palmprint. A scratched or dirty
touch surface can cause a bad image of the palmprint. A disadvantage of this
type of sensor is the fact that the imaging capabilities are affected by the
quality of skin on the palm. For instance, a dirty or marked palm is difficult to
image properly. Also, it is possible for an individual to erode the outer layer of
skin on the palmtips to the point where the palmprint is no longer visible. It can
also be easily fooled by an image of a palmprint if not coupled with a "live
palm" detector. However, unlike capacitive sensors, this sensor technology is
not susceptible to electrostatic discharge damage. Palmprints can be read from
a distance.

Ultrasonic :
Ultrasonic sensors make use of the principles of medical ultrasonography in
order to create visual images of the palmprint. Unlike optical imaging,
ultrasonic sensors use very high frequency sound waves to penetrate the
epidermal layer of skin. The sound waves are generated using piezoelectric
transducers and reflected energy is also measured using piezoelectric
materials. Since the dermal skin layer exhibits the same characteristic pattern
of the palmprint, the reflected wave measurements can be used to form an
image of the palmprint. This eliminates the need for clean, undamaged
epidermal skin and a clean sensing surface.

Capacitance :
Capacitance sensors use principles associated with capacitance in order to
form palmprint images. In this method of imaging, the sensor array pixels each
act as one plate of a parallel-plate capacitor, the dermal layer (which is
electrically conductive) acts as the other plate, and the non-conductive
epidermal layer acts as a dielectric.

Passive capacitance :
A passive capacitance sensor use the principle outlined above to form an
image of the palmprint patterns on the dermal layer of skin. Each sensor pixel
is used to measure the capacitance at that point of the array. The capacitance
varies between the ridges and valleys of the palmprint due to the fact that the
volume between the dermal layer and sensing element in valleys contains an
air gap. The dielectric constant of the epidermis and the area of the sensing
element are known values. The measured capacitance values are then used to
distinguish between palmprint ridges and valleys.

Active capacitance :
Active capacitance sensors use a charging cycle to apply a voltage to the skin
before measurement takes place. The application of voltage charges the
effective capacitor. Theelectric field between the palm and sensor follows the
pattern of the ridges in the dermal skin layer. On the discharge cycle, the
voltage across the dermal layer and sensing element is compared against a
reference voltage in order to calculate the capacitance. The distance values are
then calculated mathematically, and used to form an image of the
palmprint. Active capacitance sensors measure the ridge patterns of the
dermal layer like the ultrasonic method. Again, this eliminates the need for
clean, undamaged epidermal skin and a clean sensing surface.

Algorithms :

Matching algorithms are used to compare previously stored templates of palmprints
against candidate palmprints for authentication purposes. In order to do this either
the original image must be directly compared with the candidate image or certain
features must be compared.

1) Pattern-based (or image-based) algorithms :

Pattern based algorithms compare the basic palmprint patterns (arch, whorl, and
loop) between a previously stored template and a candidate palmprint. This
requires that the images can be aligned in the same orientation. To do this, the
algorithm finds a central point in the palmprint image and centers on that. In a
pattern-based algorithm, the template contains the type, size, and orientation of
patterns within the aligned palmprint image. The candidate palmprint image is
graphically compared with the template to determine the degree to which they
match.

2) Palmprint authentication using a symbolic representation of images :
The SAX conversion has been widely used in solving data mining problems for 1D
time series because it is computationally effi- cient, is easy to use, and is able to
achieve a satisfactory balance between dimensionality reduction and
discriminative power retaining. In this paper, we propose a natural extension of
the SAX representation, 2D SAX, for two-dimensional data such as 2D images. We
apply this new representation to the problem of texture based palmprint
authentication for testing its effectiveness. Compared to previous palmprint
authentication approach, our method mainly has two advantages. Firstly, it is
simple to implement and the computational complexity of feature extraction and
template matching is much lower than most previous methods, so that it can be
efficiently implemented for slow mobile embedded systems. Also, experimental
data show that our method is relatively more robust to image blurring. This
property is probably useful for mobile palmprint authentication system where
images are captured using low resolution/quality embedded cameras and the
motion blur are usually inevitable due to the difficulty in user palm fixation.
Secondly, our method does not require any training of parameters so that its
performance does not rely on the selection of the training dataset and can be

easily reproduced. Experimental results show that in term of the authentication
accuracy, our method over performs most previous texture based methods, and is
comparable to the state of the art palm line bases methods. In the PolyU
database, which contains 7752 palmprints, our method can achieve an EER of
0.3% for a one to one verification experiment, and a rank one identification
accuracy of 99.90%. On the more challenging CASIA database, an EER of 0.9%
can be achieved. It should be emphasized that our method does not impose any
assumptions on the palmprint texture and only the grayscale information is used.
This indicates that our method can actually be applied to any other texture based
image recognition problems. In addition, our method provides a lot of flexibility for
practical applications. By choosing different values for SAX_Length and SAX Level,
different level of dimensionality and numerosity reduction can be achieved.
Introducing the filtering bank G also provides the possibility of utilizing more
complex texture features. This might be a possible way for further improving the
accuracy of our method. Of course, as a trade off, the computational complexity
will usually increase when more complicated filters are used. For example, if the G
is a Gabor filter and SAX Level is set to 2, the generated palmprint template in our
method is identical to the PalmCode. What is more, it is quite probable that some
effective 1D SAX data mining techniques [22], such as motif finding, indexing,
abnormity locating and classification, can also be extended to 2D SAX, leading to
many potential applications in image data mining. All these are worthy of our
future efforts.
3) Personal authentication using multi-feature technique :
Recently, biometric features have been widely used in many personal
authentication applications. Biometrics-based authentication is a verification
approach using the biological features inherent in each individual. Thus, many
access control systems adopt biometric features to replace the digit-based
password. In this paper, we propose a scanner-based personal authentication
system using the palm-print features. It is very suitable in many network-based
applications. The authentication system consists of enrollment and verification
stages. In the enrollment stage, M hand images of an individual are collected as
the training samples. These samples should be processed by the pre-processing,
feature extraction, and modeling modules to generate the matching templates. In
the verification stage, a query sample is also processed by the pre-processing and
feature extraction modules, and is then matched with the templates to decide.
whether it is a genuine sample or not. In our proposed palm-print-based
identification system, the pre-processing module, including image-thresholding,
border-tracing, wavelet-based segmentation, and ROI location steps, should be
performed to obtain a square region in a palm table which is called ROI. Then, we
perform the feature extraction process to obtain the feature vectors by the Sobel
and morphological operations. The reference templates for a special user are

generated in the modeling module. In the verification stage, we use template
matching and BPNN to measure the similarity between the reference templates
and test samples. In our experiments, the samples are verified by the templatematching and BP neural-network algorithms. In the crest experiment, three kinds
of window sizes 32 × 32, 16 × 16, and 8 × 8 are adopted to evaluate the
performance of the template-matching methodology. The multiple templatematching algorithm can achieve the accuracy rates above 91%. Both FAR and FRR
values are below 9%. Next, the BPNN architecture is adopted to decide whether
the query sample is a genuine or not. In this experiment, the average accuracy
rates are above 98% for both Sobel’s and morphological features. Besides, both
FAR and FRR values are below 2%. Experimental results verify the validity of our
proposed approaches in personal authentication.

CONCLUSION :
In this paper Palmprint recognition algorithms are reviewed. Palmprint recognition
has considerable potential as a personal identification technique as it shares most of
the discriminative features with fingerprints and in addition possesses a much larger
skin area and other discriminative features such as principal lines, ridges and
wrinkles which are very useful in biometric security. Coding based techniques have
proven to be efficient in terms of memory requirement and matching speed. Fusion
technique is recent area in which researchers used to fuse features like appearancebased, line and texture features from palm-prints, which has led to an increase in
accuracy. Recent work involves use of multiscale, multi-resolution based techniques
like wavelets and contourlets are for efficient implementation of palm print
recognition.

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