Transcript A Thermal Hand Vein Pattern Verification System

A Thermal Hand Vein Pattern
Verification System
Lingyu Wang & Graham Leedham
Forensic and Security Lab,
Nanyang Technological University, Singapore
Outlines
 Overview
 Background
 Vein patterns as biometrics
 Proposed system model for hand vein pattern verification
 Details of the system
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
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Stage I : Data Acquisition
Stage II: ROI selection and image enhancement
Stage III: Background separation
Stage IV: Skeletonization
Stage V: Matching
 Testing
 Summaries and Conclusions
Overview
 Background
 Many biometric features have been utilized for this purpose. (e.g.
fingerprints, retina pattern, voice etc.)
 Each of them has its strengths and weaknesses.
 Vein Patterns as a Biometric Feature
 A vein pattern refers to the vast network of blood vessels underneath
the skin of a certain part of a person’s body
 Properties of vein patterns
 Uniqueness : a person’s vascular patterns are distinct.
 Stability : relatively unaffected by aging, except for predictable growth as
with fingerprints. The shape of the pattern keeps unchanged.
 Tolerance to forgery : the blood vessels are hidden and much harder for
intruders to copy.
 A potentially good biometric feature…
Proposed Hand Vein Pattern Verification
System
 We proposed a new system that recognizes the human
hand vein pattern images acquired by a far-infrared
(thermal) camera, which consists of five individual stages
Image
Acquisition
Raw
Images
Image
Enhancement
&
ROI Selection
Finer
Images
Vein Pattern
Segmentation
Skeletonization
Vein
Pattern
Decision
Shape
Match
Template
Data Collection
Vein Pattern Extraction
Database
 The key difference against the others
 The system directly recognizes geometric shape of a vein
pattern by measuring its Line-segment Hausdorff Distance
against a template
Data Acquisition
Stage I
 Data of interest is limited to the vein pattern in the back of the hand
 Less invasive
 Concerns for potential integration with other hand biometrics
 Vein pattern images are captured using Far-Infrared (Thermal)
imaging technology
 Superficial veins have higher temperature than the surrounding tissues
 The thermal detector inside the Infrared camera forms images with the infrared
radiation (typically within the Far Infrared region of 8~14m) emitted by the
human body
Sample Images Collected
 Images captured in an airconditioned office environment
(20-25°C and <50% humidity)
Stage I
 Images captured in a tropical
outdoor environment (30-34°C
and >80% humidity)
Analysis on the Data Quality
 Factors affecting image
quality
 Nearness of the vein to the
surface
 Body temperature
 Unevenly distribution of heat
 Heat radiation
 Ambient temperature and
humidity
 Focusing
 Camera calibration
Stage I
 Impossible to capture the
complete vascular
network
 however, the information
contained possessed by the
superficial vein pattern is
sufficient to perform personal
verification tasks for a
reasonable sized user group
Region of Interest (ROI) Location
Stage II
 Technique proposed by Lin & Fan[1]
 Extract the contour of the hand
 Calculate the distance profile between the contour points and the
midpoint of the wrist
 Locate the landmark points (tips and valleys) of the hand
 Define a fixed sized rectangular region as the ROI
[1] Chih-Lung Lin and K.-C. Fan, Biometric Verification Using Thermal Images Of Palm-dorsa Vein
Patterns. IEEE Trans. Circuits and Systems for Video Technology, 2004. 14(2): p. 199-213.
Image Enhancement
Stage II
 Removing the speckling noise using a order statistic median filter
with a 5x5 neighborhood region.
 Reduce the effect of undesired high frequency noise with a 2-D
Low Pass Gaussian Filter with 0.8 standard deviation.
H (u, v)  e
 D2 ( u ,v ) / 2 2
 Finally, all the images are normalized so as to suppress the
possible imperfections in the image due to the sensor noise and
other effects, where the desired mean and variance of the images
are both set to be 100.

vard * ( I ( x, y )  u ) 2
ud 

var
I ( x, y )  
2
 u  vard * ( I ( x, y )  u )
 d
var
if
I ( x, y )  u
Otherwise
Vein Pattern Segmentation
Stage III
 The purpose is to separate the vein pattern from the
image background, and it is done by locally adaptive
thresholding
 The threshold is calculated at each pixel, which depends on some local
statistics of the pixel neighborhood.
 The threshold over here is set as the mean value of a local 13x13
neighborhood .
 Morphological erosion and dilation is needed to clean up the images
after thresholding.
Skeletonization
Stage IV
 The size of the vein varies as human grows, and hence, the shape
of the pattern is the sole feature for later recognition
 A good representation of the pattern’s shape is via extracting its
skeleton.
 the vein pattern images go through the thinning algorithm to obtained its
skeleton
 Pruning is taken to remove the spurs branches and clean up isolated
pixels
Vein Pattern Matching
Stage V
 The purpose is to match the geometric shape of the incoming
vein pattern against the template
 Hausdorff Distance can be calculated for the spatial similarity
of the vein patterns
 lacks local structure representation such as orientation when it comes to
comparing the shapes of curves
 Line-Segment Hausdorff Distance (LHD) is calculated instead.
H ( M p , T p )  max(h( M p , T p ), h(T p , M p ))
1
p
p
where h( M p , T p )  p  min
||
m

t
||
i
j
N m mip M p t jp T p
 Line Segment Hausdorff Distance (LHD) is firstly used in a
face matching application by Gao and Leung [2]
 Incorporates the structural information of line segment orientation
 More effective for comparisons of shapes consisting of a number of curve
segments
[2] Y.Gao and M.K.H. Leung, “Line Segment Hausdorff Distance on Face Matching”. Pattern
Recognition. 35 (2002) 361-371.
LHD For Vein Pattern Shape Matching
Stage V
 A number of sampling points
are taken on the skeleton of
 d (mil , t lj ) 



the vein pattern
d ( mil , t lj )   d|| ( mil , t lj ) 
 Using the sampling points as
d  ( mil , t lj )


end points, the shape of the
vein pattern is then
represented by a set of line
segments
 Comparisons between the
d (mil , t lj )  (Wa  d (mil , t lj ))2  d||2 (mil , t lj )  d 2 (mil , t lj )
testing pattern and the
template is carried out based
1
lm  min d (mil , t lj )
on the calculation of vector d. hl (M l , T l ) 

t T
l

m M m m M
 The directed and undirected
LHD is then defined as h and
H respectively on the rightside. Hl (M l , T l )  max(hl (M l , T l ), hl (T l , M l ))
l
i
l
l
i
l
i
l
l
i
l
j
l
Testing Results
 Database containing 108 images from 12 people (9 for each person)
 3 images for each person were selected randomly to form the templates for that
person (overall template set size 36)
 The rest is used as the testing set (size 72)
 At verification stage, three undirected LHDs (H1, H2, H3) are
computed between the testing vein pattern and the three templates.
 The Average value H’ of H1, H2 and H3 is the similarity measure.
20
18
16
14
H'
12
Geniune
Intruder
10
8
6
4
2
0
0
50
100
150
Access Attempts
200
250
300
Testing Results
 Choosing 9.0 as the threshold, all the images in the
testing set are correctly recognized
 The results of the experiment are encouraging
 However, the images are taken in a more controlled
manner
 For a real life application, the surrounding conditions are
unknown
 The image quality of the vein pattern may reduce, and as a
result, a decrease of verification accuracy can be expected
Summaries and Conclusions
 A personal verification system using the Far-Infrared vein
pattern in the back of the hand as the biometric feature is
proposed
 Unlike other approaches, the system directly recognizes
the geometric shapes of the vein patterns
 The testing results of the system are encouraging
 A potentially good biometrics to be integrated with other
hand based system to become a multi-modal biometric
system.
Alternative Imaging Technology
Stage I
 Near-Infrared Imaging
 Near Infrared region refers to the spectrum from 0.7um to 1.4 um
 The haemoglobin in veinous blood absorbs more of the incident IR
radiation than the surrounding tissue
 Thus appearing darker when viewed on a conventional video monitor
 Under Investigation…