Article
Contact-Free Multispectral Identity Verification
System Using Palm Veins and Deep Neural Network
Maciej Stanuch * , Marek Wodzinski and Andrzej Skalski
Department of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30,
30-059 Krakow, Poland; wodzinski@agh.edu.pl (M.W.); skalski@agh.edu.pl (A.S.)
* Correspondence: stanuch@agh.edu.pl
Received: 14 August 2020; Accepted: 26 September 2020; Published: 6 October 2020
Abstract:
Devices and systems secured by biometric factors became a part of our lives because they
are convenient, easy to use, reliable, and secure. They use information about unique features of our
bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding
supplementary modality while keeping the user experience at the same level. Palm vein systems are
based on infrared wavelengths used for capturing images of users’ veins. It is both convenient for
the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV
wavelengths; the images are then processed by a deep convolutional neural network for extraction of
biometric features and authentication of users. We tested the system in a verification scenario that
consisted of checking if the images collected from the user contained the same biometric features as
those in the database. The True Positive Rate (TPR) achieved by the system when the information
from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal
Error Rate (EER).
Keywords: biometrics; palm vein scanner; multimodality; convolutional neural networks
1. Introduction
Biometrics is a technique of authorization and recognition based on many characteristics of a
human body and behavior that are unique and that can be used for distinguishing one subject from
another. The power of biometrics is hidden in its simplicity and reliability. The user does not have
to remember complicated passwords that are advised to be changed from time to time. Users tend
to use the same password for many sites or to use ones that are simple so they can memorize them.
If a complicated password is required, people often write it down for the sake of convenience and
it becomes vulnerable to theft [
1
]. Biometric authorization methods are superior compared to the
aforementioned issues. The user does not have to remember any passwords and will never lose them
due to the fact that the authorization key is bound to the particular body [
2
]. On the other hand,
there is a problem related to data storage. There is always a risk of a leak of the database of users’
biometric profiles , and it could pose a big threat to the society as the biometric information cannot be
changed since it is bound to our physiology. Thankfully, it is not an issue if the database is created
with care. The biometric information should not be stored in a raw form but rather as a set of extracted
features with added noise. It should be additionally hashed with a personal key that might be changed
in case of a security issue [
3
]. Another concern worth mentioning is the problem of forging a biometric
information using different techniques. There were numerous cases where hackers were able to break
into some of the systems thanks to obtaining the biometric information and imitating it accurately [
4
].
Such a situation happened in 2014 in Germany and was a warning sign to all people developing
biometric solutions. The German Defense Minister, Ursula von der Leyen, was hacked by Jan Krissler
who used commercial photos of the Minister and used them to replicate her fingerprints [5].
Sensors 2020, 20, 5695; doi:10.3390/s20195695 www.mdpi.com/journal/sensors
