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DUAL BIOMETRIC ENCRYPTED AUTHENTICATION
USING RASPERRY PI PROCESSOR
Sivasankari Narasimhan
Assistant Professor, Electronics and Communication Engineering,
Mepco Schlenk Engineering College, Virudhunagar Dt, (India).
E-mail: sivani.sivasankari@gmail.com ORCID: https://orcid.org/0000-0002-3162-4751
Muthukumar Arunachalam
Assistant Professor, Electronics and Communication Engineering,
Kalasalingam University, Virudhunagar Dt, (India).
E-mail: muthuece.eng@gmail.com ORCID: https://orcid.org/0000-0001-8070-3475
Recepción:
05/12/2019
Aceptación:
20/12/2019
Publicación:
23/03/2020
Citación sugerida:
Narasimhan, S., y Arunachalam, M. (2020). Dual biometric encrypted authentication using Rasperry
PI Processor. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Marzo 2020, 35-49.
http://doi.org/10.17993/3ctecno.2020.specialissue4.35-49
Suggested citation:
Narasimhan, S., & Arunachalam, M. (2020). Dual biometric encrypted authentication using Rasperry
PI Processor. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Marzo 2020, 35-49.
http://doi.org/10.17993/3ctecno.2020.specialissue4.35-49
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ABSTRACT
Security is one of the main concerns in many sectors especially in banking. Many protection
mechanisms such as passwords and number locks, PIN numbers have been used to identify
the correct person. The biometric protection mechanism using ngerprints are also
implemented. To ensure more security double biometric factors are implemented in this
paper. Voice is a powerful factor to identify a speaker who is holding the account in banks. In
addition to voice, usual face biometric features also considered for security in bank lockers.
Both are transformed into encrypted format and stored to avoid database hacking. In this,
Raspberry Pi board is used for implementation. To manipulate voice, devices like USB
microphone and sound cards are used. For processing face image Raspi Cam is used. When
the given image and voice matches with that of the image and voice stored in the database,
then login process starts else the person trying to unlock the locker is not the bank account
holder. For new users, signup process will be provided by administrator by capturing voice
and face images for enrollment. This system can be helpful for maintaining the customer’s
condentiality in bank lockers.
KEYWORDS
Authentication, Face recognition, Voice recognition, Encryption, Enrollment.
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1. INTRODUCTION
The most basic requirement of any bank locker is high security and getting high privacy
regarding bank locker. Every person has precious accessories like jewelry or cash in it,
so authentication of the person who wants to use the locker is very important. Eective
security can be provided by using face and voice recognition biometrics. In olden days
secret key is used by customers. Now-a-days customers’ biometric attributes are additionally
included which are unique and act as one identity for individual. A secret key can be stolen
or changed. But biometric characteristics won’t be changed, for example, an individual’s
face or voice can’t be changed or imitated. The distinguished protocol for the execution
of a bank locker security framework, with the authentication of human face and voice
recognition, to conrm the person’s character has been proposed in this paper.
The database creation phase for banking utilizes image and voice of the client to be stored
using Raspberry pi. The access to open the locker is provided only to the authorized
customers. If the image and the voice are not present in the database, the access permission
is denied.
2. RELATED WORKS
Sahani, Nanda, Sahu and Pattnik (2015) proposed a remote access control framework
for smart home condition. Raspberry Pi based entry to control and design home security
framework through site page with ZigBee is implemented. The framework distinguishes
the visitor’s quality and exchanges the picture through email and SMS by GSM to already
stored numbers. The client can specically login and cooperate with the inserted gadget
progressively without the need to keep up an extra server.
Baby, Munshi, Malik, Dogra and Rajesh (2017) proposed an empowering mechanism for
home automation with web application for electrical apparatuses (such as fan and light)
control. They are dependent on sensor inputs to indicate movements and temperature.
The lock can be controlled by giving voice directions. Thus, utilizing this framework, it is
currently progressively advantageous to control the machines in homes.
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Kaur, Sharma, Jain and Raj (2016) proposed an automation system using voice. With voice
as information, the system interprets or follows the importance of that input and creates a
proper voice yield. Utilizing voice as information, it tends to be changed over to content.
This work experiences the disadvantage that just predened voices are feasible, and it can
store just restricted voices. Subsequently, the client can’t get the full data.
Senthilkumar, Gopalakrishnan and Sathish Kumar (2014) wished-for image capturing
system based on Raspberry Pi. Face acknowledgment is the principal concern and has the
least false acknowledgment rate. The structured stage gains the pictures and stores them
into the ongoing database, which is later utilized for contrasting the principles of the clients.
Shah, Patel and Patel (2018) develops a model for storing the data in computers using
Rasperry PI. It can be programmed with languages like JAVA, HTML, .NET, Python
in it. Rasperry PI and digital signal controller (DSC) is designed for monitoring multiple
parameters based on Ethernet.
Ramani, Selvaraju, Valarmathy and Niranjan (2012) projected a secure bank locker system
based on RFID and GSM. In this framework, true individual can recover cash from bank
locker. This is used to approve the client and open the entryway continuously for bank
locker secure access. This is more secure than dierent frameworks. The RFID examines
the ID number from detached tag and send to the microcontroller, if the ID number is
legitimate, at that point microcontroller send the SMS and ask for the conrmed individual
portable number. The secret code is necessary to open the bank locker. If the individual
sends the secret word to the microcontroller, it will check the passwords entered by the
console and get veried from the cell phone. If these two passwords are coordinated, the
locker will be opened else it will be stay in bolted position. This framework is more secure
than dierent frameworks since two passwords required for conrmation.
Our project gives the following signicant works:
• With face and voice recognition for accessing the bank locker account.
• Login page to unlock the locker of the bank account holder.
• Signup page for a new user.
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• Encrypted database for storing the voice and facial features.
The remaining sections are organized as follows: section 3 provides the proposed methods
and section 4 gives the implementation results followed by conclusion in section 5.
3. PROPOSED METHODOLOGY
The main module in our processor is Rasperry Pi kit which collects all details regarding
biometric and customer’s details. Raspberry Pi 3 is used for programming to create login
and signup web pages by coding in PHP, capturing images, recording voice, creating
databases for storing the necessary details, performing image encryption process and to
perform voice and image recognition.
Keyboard
Mouse
USB Microphone
Raspi Cam (Image
Acquisition Camera)
Sound card
Camera
interface
Power
supply
SD Card
Raspbian
OS
Database
Web page
supported by
PHP (Monitor
Display)
USB
HDMI
Raspberry Pi 3
Model B
(BCM 2837)
Socket
Slot
USB Microphone
Input Voice
Feature
Extraction
(MEL)
Comparison
Data base
Identified Speaker
Figure 1. Overall block diagram.
Raspbian Stretch OS is used by this kit. The modules connected with Rasperry kit is shown
in Figure 1. Now let us see the process involved and used components in the encrypted
authentication process one by one.
3.1. ENROLLMENT AND AUTHENTICATION PROCESS
Bank customers account number, type of account and the persons involved in the particular
ID and their facial biometric features, voice features have been collected in the process of
new user enrollment. In bank database, they are stored in encrypted form.
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During login phase the customer must provide all the details to open the locker. If the
details match with the database, then the locker will be opened; otherwise the person trying
to open the locker is blocked by bank and alert is given to police station also. Sometimes
voice features do not get matched and the facial biometrics gets matched, then there will be
some likelihood that he/she may be the customer. But if face does not match, he should not
be allowed to access the locker. Because face is an important feature in any individual. But
voice may vary due to some unavoidable situations like cold, fever.
3.2. FACE RECOGNITION MODULE
Camera module captures image when capture image button is pressed in the webpage.
When the button is pressed, the python code for capturing image should run. While storing
that image in the database during signup, the image can be encrypted for better security.
This 8mp camera module is equipped for 1080 pixel video and still pictures that associate
straightforward to Raspberry Pi. The camera module associates with the Raspberry Pi
board through the Camera Serial Interface (CSI) connector to interface with camera.
The CSI transport is prepared to have high information rates, and it only conveys pixel
information to the processor. The picture of Raspi camera is portrayed in Figure 2.
Figure 2. Raspi camera.
From the continuous pictures face image must be detected and recognized. Face detection
is performed by HAAR Cascade Classiers (Tabora, 2011). There is eye, head, and
mouth and nose detectors in the HAAR cascade classiers. Detected and processed face is
compared to a database of known faces, to decide who that person is. Face Identication
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can be performed reasonably dependably, for example, with Open CV’s face Identier,
working in about 90-95% of clear photographs of an individual looking forward at the
camera. The preprocessing is done to eciently recognize the face of the customers. For
that preprocessing, Eigen Face methodology concept is applied.
It is normally harder to identify an individual’s face when they are seen from the side or at
an edge, and occasionally this requires 3D Head Posture Estimation. Principal component
analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert
a set of observations of possibly correlated variables into a set of values of linearly
uncorrelated variables called principal components. If the image elements are considered
as random variables, the PCA basis vectors are dened as eigen vectors of the scatter matrix
(ST) dened as:
(1)
where µ is the mean of all images in the training set and xi is the ith image with its columns
concatenated in a vector.
3.3. VOICE RECOGNITION MODULE
Voice authentication is implemented in Raspberry Pi in order to add an extra layer of
security. Raspberry Pi does not have a sound card and therefore it won’t support microphones
on audio jack, so we should use a USB microphone. Hence some additional modules are
installed in Python for recording voice to perform voice recognition. The recorded voice
should be of maximum 3 seconds duration. The customer can speak any of his/her secret
code in their own tone. Voice recognition is done by matching the pitch of the captured
speech signal and the speech stored in the database. Basic process is shown in Figure 3.
Keyboard
Mouse
USB Microphone
Raspi Cam (Image
Acquisition Camera)
Sound card
Camera
interface
Power
supply
SD Card
Raspbian
OS
Database
Web page
supported by
PHP (Monitor
Display)
USB
HDMI
Raspberry Pi 3
Model B
(BCM 2837)
Socket
Slot
USB Microphone
Input Voice
Feature
Extraction
(MEL)
Comparison
Data base
Identified Speaker
Figure 3. Voice biometric processing.
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Microphone is used to capture the voice of the customer. It is a transducer that changes
over sound into electrical signal.
Figure 4. (a) Sound card (b) Microphone
Components used in our voice processing are shown in Figure 4. Raspberry Pi kit does not
have an internal sound card. Also, the voice signal must be amplied prior to be given as
input to the processor. For all these purposes an USB sound card must be used in between
the USB microphone and the kit.
The sequence of steps followed in voice processing is:
• Frame the signal into short frames.
• For each frame, periodogram, power spectrum is calculated.
• Mel lter bank is applied to the power spectra.
• Energy is summed in each lter.
• DCT of Logarithm of all lter bank energies is taken.
• DCT coecients 2-13 are kept and the remaining things are discarded.
In certain cases, the image may get matched, but the voice may not get matched. These cases
may arise because of an individual’s personal conditions. These situations are unavoidable.
In such cases, the algorithm must be designed in such a manner that at these situations, the
concerned person must be allowed to login by satisfying some threshold.
3.4. ENCRYPTION
The image obtained from RASPI camera is encrypted with AES algorithm before saving
it in database. The Advanced Encryption Standard (AES) is a symmetric-key block cipher
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algorithm with Cipher Block Chaining Mode. As usual with the normal AES algorithm
(Stallings, 2005) Substitute bytes, shift rows, Mix columns, Add round keys operations are
taken place, Encrypted facial biometric data is stored in database.
4. IMPLEMENTATION
Figure 5. Hardware Setup.
Through the USB ports the keyboard, mouse and the sound card with which the microphone
connection are to be made are connected. A High Denition Multimedia Interface (HDMI)
to Video Graphics Array (VGA) connector is used to connect the processor to the monitor.
An SD card is inserted in the slot provided at the right side. Raspi Camera module is
connected to the Raspberry Pi camera interface. Hardware set up is shown in Figure 5.
The face recognition module is to capture images through the Raspberry Pi camera. The
images get stored in database which is created. The images shown in Figures 6(a) and 7(a)
are registered face, which is stored as encrypted form as shown in Figures 6(b) and 7(b).
Figure 6. (a) Captured Image 1 (b) Encrypted image.
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Figure 7. (a) Captured Image 2 (b) Encrypted image.
The face image from the passport size photo is located rst and facial image is encrypted
and stored in database. By using python coding for face recognition, rst we are detecting
the face and then the captured image is compared with the image that has already been
captured and stored in the database. The stored images should be optimized because of the
total data storage capability of the system. Since the encrypted image stores much space
than the captured image, they must be compressed and then must be stored. The database
contains the details of all the registered customer details. The database must be created
through MySQL. The details to be stored are the customer ID, customer name, customer
image and the customer voice. The sample login page created in our work is shown in
Figure 9.
Voice Recognition Module: The audio signal input should be more or less 3 seconds of
a wave (.wav) format le. Because for authentication due to the storage space constraints,
there is limitation imposed on the length of the audio signal. That audio signal must be a
code word of the customer of his own desire. The pitch values only will be compared for
authentication. The sample voice images are shown in Figure 8.
Login page (shown in Figure 10) has been created for the customer to login to access his/
her bank locker if he is an already registered user. This login page asks for customer id,
customer image and the customer voice. The customer image and voice are given as real
time data. If the image is not registered and have customer ID and ask for authenticity
means he/she will be marked as intruder.
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4.1. MATCHING
The fresh images taken at the time of verication are compared in time by time manner
with the stored templates for both voice and face image in our work.
Our work has been compared with others with methodology. Certain works have been
designed for some intended purposes and they are designed to meet that. and the comparison
of some works is given in Table 1.
Table 1. Comparison of previous works.
References
Biometric
trait
Additional
Hardware
Module used
Algorithm Intended Actions
Sahani et al.
(2015)
Face
GSM/GPRS
module (For
transferring
information to the
owner)
Eigen
Methodology
The photograph of person enter into house is
captured and sent to the owner for allow/deny
Baby et al. (2017) Voice Rasperry PI
Voice to text
and database
storage
To close the home, switch off the lights and
fans.
Kaur et al. (2016) Voice Wiki, iCloud id Voice to text
It searches the missed iPhone, Helps to search
the movie, helps to search Wikipedia,reading
news,describe weather.
Gyulyustan &
Svetoslav (2017)
Voice Rasperry PI
Hidden Markov
Model
Speech recognition with intended words and
carry out the action behind that.
Senthilkumar
(2014)
Face EICSRS platform
Eigen faces
methodology
If the user is not in the stored template, reject
the user.
Kishore Bhanse &
Jaybhaye (2018)
Front image Google API
Machine
Learning, Neural
network
To alert the user regarding correct user, or
intruder.
Proposed
Image and
voice
Rasperry PI
Eigen
Methodology,
AES (image
Encryption)
Both image and voice database information’s
are stored in the encrypted format to avoid the
hackers template hacking.
5. CONCLUSION
This work proposes the design and the development of an interactive smart bank locker
security system with the raspberry pi as the processor. The PC used for interaction can be
replaced with low-cost processors which would provide the administrator with parameters
of the entire remote device. This setup can be implemented in banking sectors for improved
security of bank lockers. It can be used to avoid access of unauthorized persons. It can be
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easily used to track the intruders. Since, face and voice both the important features are used
as the key factors, it provides an as an excellent security system. It reduces the risk of threat.
Since encryption algorithms are employed, the customer images can be stored securely.
As a future scope, a separate application can be created to send the picture of the
unauthorized customer through E-mail or through any other active social media in which
the customers will be active and alert them with this intruder information. Also, the voice
of the customer to be stored can be encrypted and then can be stored in the database. This
will be an additional factor to enhance the security of bank lockers.
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