IRJET-Computer Aided Touchless Palmprint Recognition Using Sift

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 06 | June 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2618
Computer Aided Touchless Palmprint Recognition Using SIFT
Neha Shukla1, Abhishek Saxena2
1 Research Scholar, Dept of CSE, BIET, Lko, India
2Associate Professor, Dept of CSE, BIET, Lko, India
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Abstract : Palmprint identification has established to be
one of the in style and promising biometric modalities
for rhetorical and business applications. In recent years
the Touchless system emerged as a viable choice to
address healthful problems and improve the user
acceptance. The presence of important variations in
scale, rotation, occlusion and translation in the
Touchless palpitation images requires the extraction of
characteristics that are tolerant of such changes.
Therefore, the use of traditional handheld extraction
methods in Touchless image schemes remains a hot topic
of research and therefore, all the most popular methods
for extracting the handheld function may not be useful in
Touchless frames. In this document, we systematically
perform the feature extraction method using SIFT
related to the recognition of palpable Touchless
impressions and present the performance evaluation of
images in real time. Our experimental results in more
than 15 real-time handheld images. It suggests that the
transformation characteristics of scaling invariant
features (SIFT) have significantly better performance for
Touchless handheld imaging compared to other
approaches previously employed in conventional
handheld imaging. The accuracy achieved shows a good
performance of these characteristics.
Keywords : Biometrics,Palmpriimaging,SIFT,Matlab
basics,Feature extraction.
I. INTRODUCTION
Hand-based biometric authentication systems have gained
a lot of attention since the last decade. Identity
authentication based on hand geometry such as DNA,
fingerprints, venous patterns and knuckles are among other
biometric data apart from the handheld impression,
however square measure later thought of higher and a lot of
economists. It required sensors and did not require much in
security applications such as home security systems, IT
systems, restricted access control, etc. Hand-based systems
range from earlier systems based on drive pins for non-
stick systems. To handle hygiene problems and improve
user acceptance, contactless systems have emerged as a
viable possibility.Handheld functionality is one of the
appropriate personal authentication systems for network-
based applications. The authentication system consists of
registration and verification phases. Upon registration,
training samples are collected and processed by processing,
extracting features and modeling modules to generate
masking templates. In the verification, a query sample is
also processed by preprocessing and the feature extraction
method and then it is combined with reference models to
decide whether it is a sample or not. A configuration system
consisting of a palm print-based authentication system can
work with a multi-use camera in uncontrolled
circumstances, such as mounting on a laptop, a mobile
device. These systems have great value and features can be
extracted with less computational effort.Not like previous
biometric systems, it doesn't need instrumentation and has
achieved a higher precision value equivalent to the
fingerprint. We use the SIFT method, the invariable function
of scale transforms (or SIFT) is an associate formula in
artificial vision to find and describe the local characteristics
of the images. Applications embody seeing, robotic mapping
and navigation, image handicraft, 3D modeling, gesture
recognition, video chase and matching the motion. You can
extract attention-grabbing points from the thing to produce
a "functionality" Applications embody visual perception,
robotic mapping and navigation, image handicraft, 3D
modeling, gesture recognition, video trailing and motion
correspondence. For any object in a picture, you'll be able to
extract attention-grabbing points on the thing to produce a
"function not solely detected, however additionally
characterized by values to acknowledge (match) these
areas or points of interest in different pictures of constant
scene object. Has had significant success within the
community vision.

II. OUR WORK
In this document, we investigate the method of extracting
characteristics using the characteristics of SIFT. We have
implemented the SIFT algorithm, which aims to have a
series of images and find the best match with a single image
that remains as a "template image". SIFT offers matches and
exchange scores, where the "matches" represent the same
descriptors found within the 2 images and the "scores"
determined by the surveyor's methodology, tend to
evaluate the best correspondence between all the images
with the image of the model, when there is an exact
correspondence between two images, the "score" seems to
be zero, since the position of the descriptors in the two
images is the same, we find the best match or the second
best match with the model that uses the "scores". This
approach has been adopted to address the wide variety
within the contactless image class.
III. SYSTEM DESIGN
A manual fingerprint recognition system usually consists of
four parts: manual scanner, preprocessing, feature
extraction and association. The Palmprint scanner is for
collecting portable print images. Pre-processing consists in
configuring a coordinate system to align the images of the
palm of the hand and segment a part of the palm image to
extract the characteristics. The extraction of the
characteristics is to obtain effective characteristics of the
pre-treated palms. Finally, a matchmaker compares two
characteristics of the palm of the hand.
Preprocessing
Preprocessing is used to adjust the different images on the
handheld and to divide the vocal parts to include extraction.
Most preprocessing calculations use the key between the
fingers that make up an organization's framework. The
preprocessing includes in principle five basic
improvements,
 Read a photo
 Feature extraction
 Extract the shape of a hand and, in addition, the fingers
 Detect the main keys
 Establish a coordination framework
 Matching current work image with database image to
separate the separation between two key points.
IV. FEATURE EXTRACTION USING (SIFT)
ALGORITHM:
1. Detection of scale extrema points: In this first phase,
the key points were found in SIFT. The key points are
such that they are invariant to change scale and several
views of the same object. In order to achieve said scale
space of an image, the image and the
defocusing of the Gaussian defocus
are convolved at different scales and, therefore, the
difference is taken in consequence of the blurred
Gaussian image for the efficient detection of stable key
points. The convolution of the input image with the
Gaussian blur on the scale, that is, the Gaussian
difference (DoG) at multiple scales is calculated by
subtracting two adjacent image scales separated by a
constant multiplicative factor k?
2. Location of the key points: on the stage many
candidates unstable points that have a low contrast /
are misaligned at the edges. In this phase they are
eliminated. To do this, we first have to find the
interpolated positions of the extremes using the
expansion of the Taylor series of the spatial scale
function. Interpolation is possible using the expansion

of the Taylor series of the spatial scale function DoG,
with the candidate key point as the origin. This
expansion of Taylor is given by:
Where D and its derivatives are evaluated at the
candidate key point and represent the
displacement from this point.
3. The key point of low contrast is discarded: In the
previous method, there are key points of low contrast,
which are eliminated in this stage. To discard low-
contrast key points, the value of Taylor's second-order
expansion D (x) is calculated in the displacement. If this
value is less than 0.03, the candidate key point is
discarded. Otherwise, it is maintained, with the final
position y + x and the scale, where y is the original
position of the key point on the scale.
4. Eliminate edge responses: Even if the candidate key
point is not robust enough for small amounts of noise,
the DOG function has strong responses along the edges.
Therefore, to improve stability, key points with ill-
defined positions but with high-level responses are
eliminated.
5. Assignment of orientation: In this step, each key
point is assigned with one or more orientations
according to the gradient directions of the local image.
With this step, the rotational invariance is achieved
when the key point descriptor is represented in
relation to this orientation and then the invariance to
the rotation image is achieved. The magnitude and
direction calculations for the gradient are made for
each pixel in a neighboring region around the key point
in the Gaussian image L. blurred.
6. Descriptor of key points: the previous steps have
identified positions of key points in specific scales and
assigned orientations. This ensured the invariance of
the position, scale and rotation of the image. Now we
want to calculate a descriptor vector for each key point
so that the descriptor is highly distinctive and partially
invariant with respect to the remaining change
Fig.6:Steps of SIFT Algorithm
Matlab Simulation:
Our simulation required:
a) Resize image captured by mobile phone
b) SIFT algorithm
c) Databse with real palm images
d) Matlab 2010B installed in PC

TABLE 1-Related work on touchless palmprint recognition
Accuracy:
TP= 12
TN= 3
FP=0
FN=1
Accuracy= TP+TN/(TP+TN+FP+FN)*100%
=(15/16)*100
= 93.75
Image name Recognition Result Type Processing Time(s)
Im1 Recognized True Positive 7.42
Im2 Not Recognized False Negative 6.55
Im4 Not Recognized True negative 7.73

Simulation Result
Analysis:
In this experiment using SIFT,about 16real palmimages
were stored in database and current image are
matchedwith database images.This figure shows the
maximum matching and occurance of highPerformance.
Fig.9:Performance graph using SIFT method
Fig: shows the following parameters
TP:true positive
TN:true negative
FP:false positive
V.CONCLUTION:
In this research work, realtime palmprint bases recognition
system has been envisaged. The Palmprint are classified
and features are extracted. Scale Invariant feature
transform method has been used to extract regions from
the palmprint images taken from camera images. It is
advancement from the the previous work in the sense that
previous reserach work researches have used existing
databases of palmprint images rather than realtime images
to implement and evaluate their algorithm while in this
research work actual images of palmprint are taken.16
different palmprint images are taken of 16 subjects and
algorithm is evaluated for accuracy and sensitivity of the
algorithm which shows good results.
VI. FUTURE WORK:
 Need to perform additional experiments to use the color
information available in your handheld images, to
dynamically take advantage of a larger footprint area or to
develop algorithms that can detect (even correct)
cosmetics, injuries or palm text to enable Greater precision.
 Additional work is needed to evaluate the accuracy of
correspondence in a more demanding way correspondence
protocols, i.e,combination of left and right palm.
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IRJET-Computer Aided Touchless Palmprint Recognition Using Sift

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IRJET-Computer Aided Touchless Palmprint Recognition Using Sift