Personal Authentication using Hand Geometry and
Palm Print
Shefali Sharma , Satish Singh , Rajiv Saxena
Department of Electronics and Communication,
Jaypee University of Engineering and Technology, Guna, India
Abstract — Biometrics which is used for identification and
verification have been researched and tested for a few decades,
but have only recently entered into the public consciousness
because of high profile applications and has gained importance in
today’s society where information security is essential. Hand
geometry based biometric systems are gaining acceptance in low
to medium security applications and that’s why we have
combined palm print and hand geometry for more efficient
biometric system. Hand geometry based authentication
/identification systems utilize the geometric features of the hand
like length and width of the fingers, area of hand, perimeter of
palm etc and palm print features like energy, entropy, principal
lines etc. Euclidean distance is used for matching. Experimental
results, up to 98% rate of success in identification and 100% in
verification, show the possibility of using the system in
medium/high security environments with full acceptance from all
users.
Keywords — Hand geometry, Palm print, Euclidean distance ,
Verification, Identification, Hand geometry features, Palm print
features, feature level fusion
I.
INTRODUCTION
Biometrics
is the science of measuring human’s
characteristics for the purpose of authenticating or identifying
the identity of an individual. Two types of characteristics are
measured in biometric technology namely, physiological
characteristics and behavioral characteristics. Physiological
characteristics measure human body parts while behavioral
characteristics measure the actions produced by human such
as sound, signature, or posture. Behavioral characteristics are
more vulnerable to change than the physiological
characteristics.
Several types of physiological characteristics used in
biometric are face, hand geometry, fingerprint, iris and palm
print, etc. Palm print biometric has advantages over other
types of biometric system. The palm print acquisition device
costs lesser than the iris scanning device. Palm print is harder
to imitate than fingerprint. [1]
Palm print contains the numerous amount of information when
compared to hand geometry and fingerprints. Palm print
features vary little over time. It is having high user
acceptability and even with a low resolution device palm print
is easily captured. [2]
Palm print and Hand geometry authentication system consists
of five stages: Image acquisition, Pre-processing, Feature
extraction, Matching and Decision Module as shown in fig 1.
Fig 1: Block Diagram
II.
IMAGE ACQUISITION
The sample right hand images are acquired using document
scanner: HP SCANJET G3010 in a uniform bright intensity
inside a room. Users are instructed to keep the palm on the
bottom part of the scanner rather than at the top so that most
part of the wrist falls outside of the scanning range. The
fingers are required to spread apart as possible. No pegs are
used to align the hand.
The images captured are 1700*2340 pixels color photograph
of 200dpi in JPEG format as shown in fig 2.
Total of 250 images are acquired from 50 different users, 5
images of each user.
Fig 2: Captured Image
III.
PRE-PROCESSING AND FEATURE EXTRACTION
A. Pre-processing
It involves five steps:

Image resizing

Binarizing palm image

Noise removal

Boundary tracking

Extracting region of interest (ROI)
(a)
(b)
(c)
(d)
1. Image resizing
The captured image is resized to 256*256 i.e. an image
consists of 256 rows and 256 columns respectively as shown
in fig 3(a).
2. Binarizing palm image
The resized image is converted into the binary image using
Otsu’s Algorithm. The algorithm assumes that the image to be
binarized contains two classes of pixels i.e. foreground and
background and then calculates the optimum threshold
separating the two classes pixels according to minimum intra
class variance or within class variance. A pixel whose
intensity is above the threshold is considered as white
(foreground) and rest as black (background). Binarized image
is shown in fig 3(b).
3. Noise removal
The binarized image consists of noise as well. To remove the
noise we use the fact that the size of noisy patches (number of
pixels) in the image is very less than the useful data. Therefore
we delete all those pixels whose size is less than some
threshold decided according to the experiments. Noise free
image is shown in fig 3(c).
(e)
Fig 3: (a) Resize image, (b) Binarized image with noise, (c)
Binarized image without noise, (d) Edge image, and (e) ROI
extracted marked with rectangular box.
B. FEATURE EXTRACTION
After preprocessing, the resulting image is a contour of the
hand print. This helps us to extract different features of hand
geometry.
4. Boundary tracking
Edge of the hand print is calculated using sobel mask. Sobel
edge detection technique calculates delta of two gradient
values which are in x and y direction respectively with the
help of two kernels defined. A pixel is said to be a boundary
pixel if delta of that pixel is greater than the threshold. Edge
image is shown in fig 3(d).
5. Extraction region of interest (ROI)
C: Centroid
For extracting ROI, firstly valley points are detected and with
the help of those ROI is extracted from a grayscale image of
resized image. ROI can be circular, squared or any other
shape, we have considered square because of its simplicity.
Extracted ROI is shown in fig 3(e).
VPI = Ith valley point
TPI = Ith tip points
Fig 4: Hand geometry features
 Finger length
Distance between tip point and the midpoint of the
adjacent valley points
 Thumb length
Distance between tip point and the adjacent valley
points
 Finger width
Distance between adjacent valley points
 Palm width
Distance between 2nd and 6th valley points as shown in
fig 4.
 Centroid-Tip distance
Distance between centroid and the tip points
 Centroid-Valley distance
Distance between centroid and the valley points
 Area
Number of pixels whose intensity is 1 in fig 3(c).
 Perimeter
Number of pixels present in the boundary as shown in
fig 3(c)

Energy
Step 1: The average intensity Iavg is now modified
to remove the centre pixel intensity as shown:
m
I avg 
u



Discrete Cosine Transform
For the extracted ROI, DCT is applied. The DCT
coefficient obtained is separated into four subs –
regions. DCT coefficients in each sub-region is
squared and summed to obtain the DCT energy. The
DCT energy feature is arranged to form a feature. [3]
Fast Fourier Transform
For the extracted ROI, FFT is applied. The FFT
coefficient obtained is separated into four subs –
regions. FFT coefficients in each sub-region is
squared and summed to obtain the FFT energy. The
FFT energy feature is arranged to form a feature.
Discrete Wavelet Transform
For the extracted ROI, DWT is applied. The DWT
coefficient obtained is separated into four subs –
regions. DWT coefficients in each sub-region is
squared and summed to obtain the DWT energy. The
DWT energy feature is arranged to form a feature. [3]
( mxn)  1
 1
ij
I (i, j )  I avg
I
(4.1)
max
Step 4: The Energy is computed as shown:
m
n
u
i 1
Palm print image contains various types of features. Since
texture features and line features required low resolution
image and can distinguish people effectively, these features
are investigated in this study. The feature representation must
be short but contains vital information that can differentiate
different individuals [1]. Palm print features are:
2 points of ROI (extracted from the 3 principal lines)
This feature extracts the three principal lines form the
extracted ROI. Since the number of pixels in the lines
is limited, we can take the mean of the pixels and use
it as a feature.
i i j 1
C is the gray level intensity of centre pixel.
Step 2: The maximum intensity, I max (i,j) is found.
Step 3: The fuzzy membership function u is taken as
shown:
Eg =

n
( I (i, j ))  C
j 1
2
ij
mxn
(4.2)
Where (mxn) are the total number of pixels in a
window.

Entropy
Step 1: The membership function μ is calculated from
equation 4.1
Step 2: The Entropy function is calculated as shown:
En =

1
m
n
(uij log uij  1  uij log 1  uij )

i 1  j 1
(m * n) log 2
(4.3)
Where (mxn) are the total number of pixels
in a window
IV.
RESULTS
False Acceptance Rate (FAR) is the percentage of wrongly
accepted individuals over total number of wrong matching.
False Rejection Rate (FRR) is the percentage of wrongly
rejected individuals over the total number of corrected
matching. [1] Equal Error Rate (EER) is the point where FAR
is equal to FRR.
Hand geometry result:
Fig 7: FAR and FRR vs Threshold
Fig 6: FAR and FRR vs Threshold
EER is 0.04993 at threshold 336.5.
Identification efficiency: 92% i.e. out of 250 images 230
images are correctly identified.
Verification efficiency: 100% i.e. out of 250 images 250
images are correctly verified.
EER is 0.04 at threshold 193.6.
Identification efficiency: 98% i.e. out of 250 images 245
images are correctly identified.
Verification efficiency: 100% i.e. out of 250 images 250
images are correctly verified.
Palm print result:
Results
Biometric method
EER
IE (%)
VE (%)
92
100
0.12
75.2
83.2
0.04
98
100
Hand geometry
0.04993
Palm Print
Fuusion of both methods
EER= Equal error rate
IE =Identification Efficiency
VE =Verification Efficiency
REFERENCES
Fig 5: FAR and FRR vs Threshold
EER is 0.12 at threshold 62.
Identification efficiency: 75.2% i.e. out of 250 images 188
images are correctly identified.
Verification efficiency: 83.2% i.e. out of 250 images 208
images are correctly verified.
Combined result:
[1]
[2]
[3]
Edward WONG Kie Yih, G. Sainarayanan, and Ali Chekima,
“Palmprint Based Biometric System: A Comparative Study on Discrete
Cosine Transform Energy, Wavelet Transform Energy and SobelCode
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Gayathri, R. and P. Ramamoorthy, “Palmprint Recognition using
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(7): 1049-1061, 2012
Madasu Hanmandlu, Ritu Vijay, and Neha Mittal, “A Study of Some
New Features for the Palm print Based Authentication,” proceedings of
the World Congress on Engineering 2011 Vol II, WCE 2011, July 6 - 8,
2011, London, U.K.