Scalable Imaging Device using Line Scan Camera for Use in
Biometric Recognition and Medical Imaging
Michal Dvořák
a
, Ondřej Kanich
b
and Martin Drahanský
c
Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
Keywords: Imaging System, Fingerprint, Hand Geometry, Line-Scan Camera, Skin Diseases.
Abstract: In this paper, a novel imaging system for use in a biometric or medical application utilizing a line scan camera
is being presented. The system utilizes a linear motion system to achieve a variable field of view as per
operators' demands, while maintaining the high resolution per unit area. Use cases are presented using several
demonstrations of possible applications. Fingerprint quality evaluation algorithms are showing applicability
as a biometric-enabled system. Dermatological application is demonstrated by using the acquired images to
perform a measurement of common nevi. Further uses in wound treatment and other biometrics such as hand
geometry recognition and palmprint recognition are discussed.
1 INTRODUCTION
Whether the goal is to perform medical imaging for
dermatology or wound care purposes, or perhaps data
for biometric applications are required, there is a need
to collect the images of a human body in a precise,
repeatable, and high-quality manner. Despite cameras
being widespread, and their parameters soaring in the
last couple of years, when the application demands
highly detailed images of large areas of the human
body, it can be quickly discovered that even the
available high-resolution cameras are insufficient. It
is then necessary to either come up with a way to
utilize the less detailed imagery or to find a way to
generate a sufficient number of high-quality 2D sub-
images that can be reconstructed into the final image.
Both methods have their drawbacks. What if,
however, instead of using a traditional 2D camera
sensor with a fixed number of pixels per area, a sensor
with variable field of view was made?
The goal of this work is to present a design and a
prototype of an optical scanning device, utilizing a
line scanner instead of a traditional 2D sensor. A
device that can change the field of view based on the
operators' needs and thus allowing for scanning body
parts of various sizes without having to change the
a
https://orcid.org/0000-0003-3265-6955
b
https://orcid.org/0000-0003-0093-8536
c
https://orcid.org/0000-0002-9321-7385
physical configuration of the device. A device that
can facilitate scanning of a finger, a hand or a whole
arm without having to sacrifice an image quality.
In this paper, a possible configuration of the
device is demonstrated by capturing images of human
hands and acquiring a small database. This decision
has been made to allow the use of biometric quality
evaluation to establish the performance of the device.
1.1 Use Cases
The intended application of this device is a medical
imagining in the visible, near-infrared or ultraviolet
spectrum with a focus on usability in dermatology
and potentially a wound treatment such as shown in
(Dick et al., 2019), (Zhang et al., 2017), (Deng et al.,
2017) and the technologies developed for this
purpose, most notably by (Korotkov et al., 2015) and
(Haeghen et al., 2000). The device was designed for
tracking the change or rate of change of selected
features such as various skin lesions and scar tissues.
1.2 Operation Principle
Unlike the traditional 2D CMOS or CCD sensor,
where during the acquisition the whole target object
is projected onto the sensor and is downloaded from
160
Dvo
ˇ
rák, M., Kanich, O. and Drahanský, M.
Scalable Imaging Device using Line Scan Camera for Use in Biometric Recognition and Medical Imaging.
DOI: 10.5220/0010342601600168
In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 1: BIODEVICES, pages 160-168
ISBN: 978-989-758-490-9
Copyright
c
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Figure 1: Line scanner principle (Fermum, 2016).
the camera after that, the proposed device utilizes a
line scanner instead.
The general operating principle of line scanner
can be seen in Figure 1. During the acquisition, the
camera captures information regarding a line whose
width w
r1
is determined by the camera and camera
lens parameters. In this paper, the axis that
corresponds to the acquired line will be described as
a sensor axis and the perpendicular axis will be called
the movement axis.
To reconstruct the complete image of the target,
either the camera or the target needs to move along
the movement axis at a predefined speed v
s
and the
acquisition must be performed at a rate of f.
𝑓
= 1/𝜏
(1)
𝜏= 1 2
∙𝑤

𝑣
(2)
Where f [Hz] is the rate of acquisition in lines per
second. 𝜏 [s] is the period between acquisitions.
w
r
[mm] is the resolvable width which is required and
v
s
[mms
-1
] is the speed of the system. The factor of ½
is necessary to meet the Nyquist–Shannon (NS)
sampling theorem (Nyquist, 1928) due to the
movement of the system.
1.3 Optical Principle
Given the distance to the target object and the size of
the chosen sensor, the camera lens needs to be
calculated. To determine the appropriate camera lens,
the required field of view fov
w
needs to be considered.
In this case, fov
w
describes the maximum dimension
(width) of the target that can be observed with chosen
optics.
𝑓𝑜𝑣
represents a field of view expressed as an
angle.
𝑓
𝑜𝑣

=2𝑠tan (𝜃/2)
(3)
𝑓
𝑜𝑣
= 2 ∙ arctan (h/2f)
(4)
s is a distance to our target, f is the focal length of
our chosen lens and h is the actual size of the sensor.
The focal distance (Ray, 2004) f can be calculated
using the required distance to the object, also known
as object distance g, and ratio of sensor size to target
size, also known as the magnification m (Ray, 2004).
𝑓
=𝑚𝑔 (1+𝑚)
(5)
The size of the smallest resolvable object 𝑤

which can be scanned in the sensor axis can be
determined from the resolution of used sensor r
s
and
the
𝑓𝑜𝑣
2
using the following formula.
𝑤

=2
𝑓
𝑜𝑣

/𝑟
(6)
Where the factor of two ensures NS sampling
theorem is met. For completeness, the following
formula derived from (2) and (3) denotes the
resolvability limit in the axis of the movement.
𝑤

=2𝑣
𝑓
(7)
For the traditional 2D sensor, the field of view in
both dimensions would need to be considered,
however, for line scanner, the field of view of the
movement axis is defined purely by the physical
dimension of the scanner in the axis of the movement.
For finite image, the field of view of the image in
the axis of the movement
𝑓𝑜𝑣

can be simply
defined as a function of time t.
𝑓
𝑜𝑣

=𝑣
∙𝑡
(8)
1.4 Requirements for Biomedical and
Biometric Applications
The most known biometric systems are touch-based
fingerprint sensors used in mobile phones or laptops.
Other devices use hand geometry, finger vein or palm
vein for the recognition process (Debiasi et al., 2018).
One thing these devices often have in common is the
necessity to touch the sensing area. Especially now,
the hygienic demands dictate that the devices used by
multiple people need to be cleaned and disinfected at
regular intervals. This paper offers an approach where
no optical element needs to be touched.
The human hand contains much information that
can be used for biometric recognition (Drahanský &
Kanich, 2019). The focus of this article is to analyze
fingerprint and hand geometry characteristics with
limited discussion concerning the feasibility of
palmprint and vein extraction.
The spatial resolution used for fingerprint
recognition is typically 500 dpi (dots per inch) for the
commercial devices but can be performed with
images with resolution as low as 300 dpi (Drahanský
et al., 2018).
Customarily, the hand is placed onto a designated
surface to limit the user’s movement. High contrast
Scalable Imaging Device using Line Scan Camera for Use in Biometric Recognition and Medical Imaging
161
images are desirable for valleys and ridges to be
easily distinguishable.
For the hand geometry, the resolution
requirements are lower than for fingerprints. For
successful hand shape extraction, the scanned area
needs to be large enough for the whole hand up to the
wrist to be visible.
For biomedical purposes, the device needs to be
calibrated to give the device an ability to measure the
size of the studied objects. The external light source
and repeatable scanning allows the comparison of the
studied objects and evaluate the rate of change in size,
colour or structure (Dick et al., 2019).
2 DEVICE DESIGN
In this part, the general requirements and consequent
design of the scanner will be discussed along with a
list of components that have been used for the
prototype described in this paper.
2.1 Optical Design
Following parameters are defined.
1. The spatial resolution of the image is to be at
least 350 dpi.
2. The scanned area is to be at least
400 × 180 mm.
The first requirement allows to resolve objects of
approximately 0.145 mm. It ensures that both the
biometric applications and the calibrated
measurement for a medical imagining application can
be performed. Both requirements are used to define
linear scanner itself, as the minimum resolution can
be derived from them in the following manner.
𝑅_𝑚𝑖𝑛 = 350 ∙ (400/25.4) 5,512
(9)
Based on this requirement, a line scanner
raL6144-16gm Basler with the resolution of 6,144 px
has been chosen as suitable.
Formula (5) can be used to find a suitable camera
lens. The object distance of the device is 535 mm, as
further discussed in subsection 2.2. Given the
dimensions of the chosen sensor and the target area
dimensions, the magnification needs to be at least
0.096 to guarantee the 350 dpi requirement. Using the
formula (5), the focal length can be calculated to be
47 mm. 50 mm camera lens has been used due to its
availability. This guarantees a resolution of 350 dpi
for the area of 417.1 mm. Camera lense AF Nikkor
50mm f/1.8D has been used in the device. The
Chromasens Corona II type C with LED-control unit
XLC4-1 has been used for illumination.
2.2 Mechanical and Electrical Design
The main computing unit is a single board computer
(SBC) Raspberry Pi 3. It has been decided that the
movement subsystem will be controlled by a separate
microcontroller (MCU) Arduino Uno.
Figure 2 illustrates how are the individual
components connected. The functionality and
purposes of individual subsystems are further
described in subsection 2.3.
Figure 2: Functional diagram of the device, illustrating
individual subsystem and communication interfaces.
For power, the device utilized three Manson NP-
9615 DC regulated power supplies to generate 48 V,
24 V, 12 V and 5 V for voltage branches. The
summary of electrical requirements for the used
components can be found in Table 1.
The conversion of rotational movement of the
stepper motor into the linear motion necessary for the
scanning has been realized using a linear motion
system with a threaded rod NL208TR-1200 made by
T.E.A. Technik (T.E.A Technik s.r.o., 2020). This
system, considering the method of scanning, allows
for objects up to 120 × 43 cm in size to be scanned
while maintaining the 350 dpi spatial resolution or in
other words, it functions as approximately 100 Mpx
industrial-grade camera.
BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices
162
Table 1: Electrical parameters of components.
Component Voltage [V]
Maximum
current [A]
raL6144-16gm 12 0.375
XLC4-1 24 3
CORONA II 24 1.8
EM705 48 -
86HS45 48 4.2
RPI3 5 2.5
Arduino UNO r3 12 -
The Figure 3 represents a front view of the
hardware setup in schematics form, along with the
physical dimensions in mm. The dimension 345 mm
represents the maximum range the scanner can move
and, therefore, also the maximum dimension of the
resulting image along this axis. During the database
collection, the users approached the device from the
front, placed the hand onto the Hand placement
platform and the scanner performed a complete image
acquisition by moving from left to right.
Figure 3: Construction design of the scanner.
Figure 4: The function calls sequence during the scanning
process.
2.3 Control System
The SBC either directly or indirectly controlled each
part of the device. Figure 4 shows the control
structure and the way in which the modules are
sequentially called.
2.3.1 Light Control
The SBC was communicating with the XLC4-1 unit
using Telnet interface over the ethernet. Series of
simple commands provided by the unit's
documentation were used to set the intensity of the
light and to turn the light module on and off.
Commands to monitor the status of the unit and the
module were also utilized.
2.3.2 Movement Control
The MCU has performed movement control to avoid
unexpected interruptions of the SBC. Using the
USART interface, a command from SBC is sent to
MCU that contains a piece of information about
requested direction and distance to be travelled. MCU
parses this command and using the HW timer and
counter, it generates a pulse sequence with a
frequency of 62.5 kHz for the appropriate number of
ticks.
EM705 stepper driver is set for micro-stepping at
12,800 steps per revolution. This, combined with the
physical property of NL208TR-1200 which performs
a lateral movement of 4 mm per one revolution, gives
us a precise function that defines the distance
travelled d in millimetres as a function of time t in
seconds in the following manner.
𝑑(𝑡) = 4 ∙ (62,500 ∙ 𝑡) 12,800
(10)
The prototype for the database collection used a
scanning time of 10,000 ms (10 s), covering
195.31 mm of distance.
2.3.3 Line Scanner Control
The line scanner is controlled by SBC using the GigE
interface and Basler API. The script developed for
this purpose handles proper initialization, data
grabbing and image construction. All triggering and
frame grabbing is performed using software scripts.
The prototype due to standard dimensions of the
target performs acquisition of 5,120 lines at a rate of
500 lines per second. Considering the movement
speed of the linear movement system, the chosen rates
cause a line width to be 0.039 mm. Due to the
apparent difference of the vertical pixel size from the
Scalable Imaging Device using Line Scan Camera for Use in Biometric Recognition and Medical Imaging
163
horizontal pixel size, the image is upscaled by a factor
of two in the x-axis prior being saved.
3 DATA COLLECTION
The choice has been made to use a visible light source
for image acquisition. For the application, it was
chosen to test fingerprint recognition which requires
very precise images of the ridges and recognition
based on hand geometry which can use both sides of
the hand.
3.1 Database Collection
Per the design decisions outlined in previous sections
of this paper, the device generates images with the
resolution of 6,144 × 5,120 px that are upscaled to
12,288 × 5,120 px for easier visual inspection. The
scanned area is 417.1 × 195.3 mm which corresponds
to the spatial resolution of 375 dpi in the scanner axis
and 666 dpi in the movement axis. In Figure 5, two
sample images are presented.
Figure 5: Example of dorsal hand surface image (left) and
palmar surface image (right).
The image collection was done without adhering
to any specific condition, in an ordinary room with
daylight and artificial light present. Volunteers came
into the room and one or both of their hands were
acquired from palmar and/or dorsal side.
Overall, 145 images were acquired. These
contained 77 hands from the volunteers in the 20-30
age group, mixed gender. 56 of these images are from
the palmar side, remaining 89 from the dorsal side.
3.2 Images Applications
It is essential to remind the reader that the device was
developed primarily as a scanning method and that
the biometric quality evaluation serves primarily to
gauge the image quality.
3.2.1 Biometric Recognition: Fingerprints
Due to large scanned area, the preprocessing aims to
localize and extract fingerprints, these subimages are
then enhanced as the algorithms for the fingerprint
evaluation, generally expecting binarized or at least
normalized data.
Hand extraction started with coarse image
alteration. In this step, a rectangle of 7,800 px to
4,300 px is isolated from position x = 4,500 px and y
= 225 px. Cropped images contain only the hand and
a part of the wrist. Images were rotated so that fingers
pointed upwards and background was thresholded
away.
Using convolution-based localization algorithms,
the probable position of fingers was determined.
Figure 6 shows result of the finger localization. After
getting the finger images, histogram equalization and
local thresholding methods are used to normalize the
image and to increase the contrast of the ridges.
Result can be seen in the Figure 7.
Figure 6: Input image (left) and isolated fingers (right).
Figure 7: Example of two processed fingerprints.
280 images containing 230 fingerprints were
acquired from the images. The fingerprints were then
evaluated using VeriFinger (version 10)
(Neurotechnology, 2018) and FiQiVi (Dejmal, 2019).
BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices
164
The median VeriFinger quality was 32 (maximum 60,
minimum 10), FiQiVi showed a median of 38
(maximum 57 and minimum 19). For biometric
recognition the VeriFinger developer
Neurotechnology recommends the quality of at least
40 for identification and 30 for verification. This has
also been experimentally verified by (Alsmirat et al.,
2018).
While the quality of the fingerprints may seem
low, it is an impressive score. Firstly, the device to
facilitate the large area acquisition used a 375 dpi
resolution, which is lower than it is usual for
fingerprint sensors. Secondly, unlike the almost
absolute majority of sensors, this device does not use
a scanning surface against which the finger is to be
pressed. Thus, it reduces the available scanning area
due to the finger geometry, as it is not pressed against
a glass surface, as well as decreases the contrast due
to the absence of total internal reflection. This
concept can be seen in (Maltoni et al., 2009) Thirdly,
this evaluation contained also thumbs which were
usually acquired at an angle. Lastly, only basic image
pre-processing was done with no fingerprint
enhancement algorithms being used. Therefore, the
fact that despite all the disadvantages, the device still
manages to meet the fingerprint biometry baseline is
a relatively strong proof of its viability.
3.2.2 Biomedical Applications
The device is mainly intended to be used for
monitoring hand diseases and disorders, such as
eczema, as well as various skin growths, such as
nevus, to monitor their size and area. In the previous
section, it was shown that the device is able to
distinguish ridges that have a width from 0.2 to
0.5 mm (Drahanský et al., 2011). Based on fixed
optics and calibrated system, precise measurements
can be performed.
Figure 8 (top) provides a detailed image of nevi
on one user’s hand. Due to the system being
calibrated, we can determine that a bounding box of
34 × 29 px and 24 × 19 px that can be used to
surround the nevi corresponds to size of
1.23 × 1.13 mm and 0.87 × 0.74 mm respectively.
The analysis of the size of the nevi can be
immediately performed as well as saved and
compared during subsequent scanning for any
change. The same process can be used to observe the
rate or any changes in the wound healing process.
This can be seen in Figure 8 (bottom) were mutilated
Figure 8: Detail of nevi (top). Regenerated friction ridges
on the fingertip (bottom).
finger is present but the current images clearly show
that the friction ridges are restored on the remainder
of the finger.
3.2.3 Biometric Recognition: Hand
Geometry
The images collected by the scanner are of sufficient
quality to perform recognition based on hand
geometry if other biometrics are unavailable, for
example, due to injuries such as a burn or skin
diseases.
As the acquisition process is calibrated and the
images can be used to perform measurements of the
target object, absolute measurements may be used as
a feature set for the recognition algorithms, such as
described by (Pititeeraphab & Pintavirooj, 2018) and
(Wirayuda et al, 2013) or relative measurement, as
described by (Siswanto et al, 2013).
Majority of algorithms perform segmentation and
work with a binarized image of the hand, which these
images can also be converted to. The feature set can
then be constructed from such features as outlined in
the Figure 9. Given the knowledge that pixel's height
corresponds to 0.039 mm and the pixel's width
corresponds to 0.036 mm. Using this information,
any selected length of the image may be recalculated
into a real-world dimension.
Figure 9: Proposed hand geometry features.
Scalable Imaging Device using Line Scan Camera for Use in Biometric Recognition and Medical Imaging
165
3.2.4 Biometric Recognition: Other
Characteristics
Due to the image size and resolution, additional
biometric features can be extracted. Figure 10 shows
the enhanced image of the palmar side of the hand
indicating a multitude of data for possible biometric
(e.g. palmprint recognition). In this paper, visible
light source has been used, however, should the other
wavelength be used, other data may be acquired. The
use of near-infra lighting near the 850 nm wavelength
would allow for an observation of veins such as
(Huang et al., 2018), again usable for biometric or
medical purposes.
Figure 10: Example of enhanced image of the palmprint.
3.3 Discussion of the Results
The presented results and images serve as a
demonstration of the viability of line scan cameras for
biomedical and biometric application. The ability to
perform scans of various lengths gives this system
flexibility the devices with traditional 2D sensors
lack. It can also achieve very good precision on the
whole scanned area. There are some disadvantages as
well. Scanned user has to remain calm during the
scanning process and line cameras can be expensive.
3.3.1 The Device as a Biometric System
In the article, fingerprint and hand geometry
recognition has been discussed and its efficiency
evaluated. It can be concluded that while the device
would need to be modified for it to be a more effective
biometric system, the values from commercial and
academic quality assessment algorithms indicate that
even as it is, the device could be used for biometric
application. This, more than anything, speaks of the
image quality received from the device and proves its
efficacy in scanning live objects which, to the
knowledge of the authors, has not been widely
explored for line scan cameras.
Applicability of the hand geometry recognition
and palmprint recognition has been discussed, while
the usability of the palmprint is implied by the fact
that the images may be used for fingerprint
recognition. There is, to the knowledge of the authors,
no available tool to measure the image quality for the
hand geometry recognition. Therefore, only relevant
research into existing literature has been done.
3.3.2 The Device as a Biomedical System
Based on the available literature, the skin images of
sufficient quality may be used to analyze various skin
growths. It has been demonstrated, both in the design
of the device and in the final images, that the skin
images offer sufficient quality to perform imaging
and extract a precise measurement of the various skin
objects. Should the scanning be repeated, a change in
time can be observed.
4 CONCLUSIONS
This paper introduces an optical scanning device
using line scan camera, linear movement system, light
unit, single board computer and microcontroller
capable of producing images of the variable field of
view without having to make concessions to the
quality due to its mechanical properties.
The laboratory prototype of the device was
constructed and pilot data collection consisting of 145
human hands, of which 56 was palmar side up, was
conducted. The function and the performance of the
device have been tested to determine its efficiency as
a biometric and biomedical system. The first test was
focused on the possibility of using acquired images
for fingerprint recognition. Overall, 280 fingerprint
images were extracted from the database and
analyzed using two fingerprint quality assessment
systems. The image quality of the majority of the
extracted fingerprints has been determined to be
sufficient for fingerprint verification and
identification.
Dorsal and palmar images of hand were examined
for the usage in hand geometry recognition as well as
performing measurements of various skin features
usable for biomedical application. These
measurements are suitable for monitoring nevi, skin
diseases analysis and a rate of change in the wound
healing process. Based on the analysis, it was decided
that the detail of the images is sufficient.
BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices
166
To sum it up, the device can obtain high-quality
images. The examined object did not have to touch
the sensing area. Size of the images is limited by the
defined width of the camera, but scalable in the
movement axis, limited only by the linear motion
system's properties. Within the limits of the motion
system, the change of the image size due to the
movement axis does not require any physical changes
to the device and can be changed programmatically
by the operator.
For further research, the device could be enlarged
and used as a full-frontal body scanner. Optimization
of the biometric extraction process would lead to
higher performance rate for the biometric applications.
ACKNOWLEDGEMENTS
This research has been realized under the support of
the following grants: Technology Agency of the
Czech Republic from the ÉTA programme "Survey
and education of citizens of the Czech Republic in the
field of biometrics – TL02000134", "Reliable, Secure,
and Efficient Computer Systems" internal Brno
University of Technology project FIT-S-20-6427.
REFERENCES
Alsmirat, M. A., Al-Alem, F., Al-Ayyoub, M., Jararweh,
Y., Gupta, B., 2018. Impact of digital fingerprint image
quality on the fingerprint recognition accuracy.
Multimedia Tools and Applications, 78(3), 3649-3688.
https://doi.org/10.1007/s11042-017-5537-5
Debiasi, L., Kauba, C., Prommegger, B., Uhl, A., 2018.
Near-Infrared Illumination Add-On for Mobile Hand-
Vein Acquisition. 2018 IEEE 9th International
Conference on Biometrics Theory, Applications and
Systems (BTAS). https://doi.org/10.1109/BTAS.
2018.8698575
Dejmal, D., 2019. Analýza systémů pro měření kvality
otisku prstů [Bachelors thesis, Brno University of
Technology]. https://www.fit.vut.cz/study/thesis-
file/19344/19344.pdf
Deng, Z., Fan, H., Xie, F., Cui, Y., Liu, J., 2017.
Segmentation of dermoscopy images based on fully
convolutional neural network. 2017 IEEE International
Conference on Image Processing (ICIP).
https://doi.org/10.1109/icip.2017.8296578
Dick, V., Sinz, C., Mittlböck, M., Kittler, H., Tschandl, P.,
2019. Accuracy of Computer-Aided Diagnosis of
Melanoma. JAMA Dermatology, 155(11), 1291.
https://doi.org/10.1001/jamadermatol.2019.1375
Drahanský, M. (Ed.), 2018. Hand-Based Biometrics:
Methods and technology. IET, ISBN 978-1-78561-224-
4.
Drahanský, M., Kanich, O., 2019. Influence of Skin
Diseases on Fingerprints. In Nait-Ali, A. (Ed.),
Biometrics under Biomedical Considerations. Springer.
https://doi.org/10.1007/978-981-13-1144-4
Drahanský, M., Orság, F., Dvořák, R., Hájek, J., Váňa, J.,
Herman, D., Kněžík, J., Marvan, A., Lodrová, D.,
Doležel, M., Hanáček, P., Mráček, Š., Stružka, J., 2011.
Biometrie. Cumpter Press s.r.o. ISBN 978-80-254-
8979-6.
Fermum, L., 2016 (n.d.). Line scan camera basics.
Retrieved November 05, 2020, from https://www.
vision-doctor.com/en/line-scan-cameras/line-scan-
camera-basics.html
Haeghen, Y., Naeyaert, J., Lemahieu, I., Philips, W., 2000.
An imaging system with calibrated color image
acquisition for use in dermatology. IEEE Transactions
on Medical Imaging, 19(7), 722-730. https://doi.org/
10.1109/42.875195
Huang, Q., Hu, K., Zhou, P., Luo, Y., Wu, L., 2018. Design
of Finger Vein Capturing Device Based on ARM and
CMOS Array. 2018 2nd IEEE Advanced Information
Management, Communicates, Electronic and
Automation Control Conference (IMCEC).
https://doi.org/10.1109/imcec.2018.8469403
Korotkov, K., Quintana, J., Puig, S., Malvehy, J., Garcia,
R., 2015. A New Total Body Scanning System for
Automatic Change Detection in Multiple Pigmented
Skin Lesions. IEEE Transactions on Medical Imaging,
34(1), 317-338. https://doi.org/10.1109/tmi.2014.235
7715
Maltoni, D., Maio, D., Jain, A. K., Prabhakar, S., 2009.
Handbook of Fingerprint Recognition. Springer,
Second Edition, ISBN 978-1-84882-253-5.
Neurotechnology, 2018. MegaMatcher 10.0, VeriFinger
10.0, VeriLook 10.0, VeriEye 10.0 and VeriSpeak 10.0
SDK – Developer’s Guide. Neurotechnology, Version:
10.0.0.0.
Nyquist, H., 1928. Certain Topics in Telegraph
Transmission Theory. Transactions of the American
Institute of Electrical Engineers, 47(2), 617-644.
https://doi.org/10.1109/t-aiee.1928.5055024
Pititeeraphab, Y., Pintavirooj, C., 2018. Identity
Verification Using Geometry of Human hands. 2018
11th Biomedical Engineering International Conference
(BMEiCON).
https://doi.org/10.1109/bmeicon.2018.8609986
Ray, S. F., 2004. Applied photographic optics: Lenses and
optical systems for photography, film, video, and
electronic imaging. Oxford: Focal Press, Third edition.
ISBN 978-0-24051-540-3.
Siswanto, A., Tarigan, P., Fahmi, F., 2013. Design of
contactless hand biometric system with relative
geometric parameters. 2013 3rd International
Conference on Instrumentation, Communications,
Information Technology and Biomedical Engineering
(ICICI-BME). https://doi.org/10.1109/icici-bme.2013.
6698492
T.E.A. TECHNIK s.r.o., 2020. Lineární osa se šroubem
NL. T.E.A. TECHNIK s.r.o. Retrieved November 04,
Scalable Imaging Device using Line Scan Camera for Use in Biometric Recognition and Medical Imaging
167
2020, from https://www.teatechnik.cz/linearni-osa-
sroubem-nl/
Wirayuda, T. A., Kuswanto, D. H., Adhi, H. A., Dayawati,
R. N., 2013. Implementation of feature extraction based
hand geometry in biometric identification system. 2013
International Conference of Information and
Communication Technology (ICoICT).
https://doi.org/10.1109/icoict.2013.6574583
Zhang, X., Wang, S., Liu, J., Tao, C., 2017. Computer-
aided diagnosis of four common cutaneous diseases
using deep learning algorithm. 2017 IEEE International
Conference on Bioinformatics and Biomedicine
(BIBM). https://doi.org/10.1109/bibm.2017.8217850
BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices
168